cutpointr/0000755000176200001440000000000014726142632012310 5ustar liggesuserscutpointr/tests/0000755000176200001440000000000014726136577013465 5ustar liggesuserscutpointr/tests/testthat/0000755000176200001440000000000014726142632015312 5ustar liggesuserscutpointr/tests/testthat/test-cutpointr.R0000644000176200001440000020362314726125516020451 0ustar liggesuserscontext("test-cutpointr.R") library(cutpointr) library(ggplot2) test_plot.cutpointr <- function(cutpointr_object) { tempplot <- plot(cutpointr_object) expect_identical(class(tempplot), c("gtable", "gTree", "grob", "gDesc")) } test_ggplot_functions <- function(cutpointr_object, do_plot_metric = TRUE) { if (do_plot_metric) { tempplot <- plot_metric(cutpointr_object) expect_identical(class(tempplot), c("gg", "ggplot")) } tempplot2 <- plot_roc(cutpointr_object) expect_identical(class(tempplot2), c("gg", "ggplot")) tempplot3 <- plot_x(cutpointr_object) expect_identical(class(tempplot3), c("gg", "ggplot")) tempplot4 <- plot_precision_recall(cutpointr_object) expect_identical(class(tempplot4), c("gg", "ggplot")) tempplot5 <- plot_sensitivity_specificity(cutpointr_object) expect_identical(class(tempplot5), c("gg", "ggplot")) } test_that("Cutpointr standard application", { data(suicide) opt_cut <- cutpointr(suicide, dsi, suicide) expect_true("cutpointr" %in% class(opt_cut)) expect_that(nrow(opt_cut), equals(1)) expect_that(sum(is.na(opt_cut)), equals(1)) # boot is NA test_plot.cutpointr(opt_cut) test_ggplot_functions(opt_cut) }) test_that("Cutpointr works with different data types", { set.seed(456) tempdat <- data.frame(x = rnorm(10), y = sample(0:1, size = 10, replace = TRUE)) opt_cut <- cutpointr(tempdat, x, y) expect_silent(summary(opt_cut)) expect_that(nrow(opt_cut), equals(1)) expect_that(sum(is.na(opt_cut)), equals(1)) test_plot.cutpointr(opt_cut) tempdat$y <- factor(tempdat$y) opt_cut <- cutpointr(tempdat, x, y) expect_silent(summary(opt_cut)) expect_that(nrow(opt_cut), equals(1)) expect_that(sum(is.na(opt_cut)), equals(1)) test_plot.cutpointr(opt_cut) tempdat$y <- as.character(tempdat$y) opt_cut <- cutpointr(tempdat, x, y) expect_silent(summary(opt_cut)) expect_that(nrow(opt_cut), equals(1)) expect_that(sum(is.na(opt_cut)), equals(1)) test_plot.cutpointr(opt_cut) # With subgroup set.seed(567) tempdat <- data.frame(x = rnorm(30), y = sample(0:1, size = 30, replace = TRUE), g = sample(0:2, size = 30, replace = TRUE)) opt_cut <- cutpointr(tempdat, x, y, g) expect_silent(summary(opt_cut)) expect_that(nrow(opt_cut), equals(3)) expect_that(sum(is.na(opt_cut)), equals(3)) test_plot.cutpointr(opt_cut) test_ggplot_functions(opt_cut) tempdat$g <- factor(tempdat$g) opt_cut <- cutpointr(tempdat, x, y, g) expect_silent(summary(opt_cut)) expect_that(nrow(opt_cut), equals(3)) expect_that(sum(is.na(opt_cut)), equals(3)) test_plot.cutpointr(opt_cut) test_ggplot_functions(opt_cut) tempdat$g <- as.character(tempdat$g) opt_cut <- cutpointr(tempdat, x, y, g) expect_silent(summary(opt_cut)) expect_that(nrow(opt_cut), equals(3)) expect_that(sum(is.na(opt_cut)), equals(3)) test_plot.cutpointr(opt_cut) test_ggplot_functions(opt_cut) }) test_that("Bootstrap does not return duplicate colnames", { set.seed(456) tempdat <- data.frame(x = rnorm(100), y = sample(0:1, size = 100, replace = TRUE)) opt_cut <- cutpointr(tempdat, x, y, boot_runs = 20) expect_true(all(table(colnames(opt_cut$boot[[1]])) == 1)) # With subgroup set.seed(123) tempdat <- data.frame(x = rnorm(300), y = sample(0:1, size = 300, replace = TRUE), g = sample(0:2, size = 300, replace = TRUE)) opt_cut <- cutpointr(tempdat, x, y, g, boot_runs = 20) expect_true(all(table(colnames(opt_cut$boot[[1]])) == 1)) }) test_that("Plotting with bootstrapping is silent", { set.seed(456) tempdat <- data.frame(x = rnorm(100), y = sample(0:1, size = 100, replace = TRUE)) opt_cut <- cutpointr(tempdat, x, y, boot_runs = 20) test_plot.cutpointr(opt_cut) test_ggplot_functions(opt_cut) }) test_that("AUC calculation is correct and works with Inf and -Inf", { tempdat <- data.frame(x = c(-Inf, 0.3, Inf), y = factor(c(0, 1, 1))) roc_cutpointr <- cutpointr::roc(tempdat, x, y, pos_class = 1, neg_class = 0) auc_cutpointr <- cutpointr::auc(roc_cutpointr) expect_equal(auc_cutpointr, 1) cp <- cutpointr(tempdat, x, y) expect_equal(cp$AUC, 1) set.seed(123) tempdat <- data.frame(x = rnorm(100), y = factor(c(rep(0, 50), rep(1, 50)))) roc_cutpointr <- cutpointr::roc(tempdat, x, y, pos_class = 1, neg_class = 0) auc_cutpointr <- cutpointr::auc(roc_cutpointr) expect_equal(round(auc_cutpointr, 3), 0.541) cp <- cutpointr(tempdat, x, y, pos_class = 1, direction = ">=") expect_equal(round(cp$AUC, 3), 0.541) }) test_that("Plotting ROC curve from roc()", { set.seed(123) tempdat <- data.frame(x = rnorm(100), y = factor(c(rep(0, 50), rep(1, 50)))) roc_cutpointr <- cutpointr::roc(tempdat, x, y, pos_class = 1, neg_class = 0) tempplot <- plot_roc(roc_cutpointr) expect_identical(class(tempplot), c("gg", "ggplot")) }) test_that("Correct midpoints are found", { temp <- data.frame(x = c(-Inf, 1, 2, 3, 5, Inf), y = c(1, 1, 1, 0, 0, 0)) optcut <- cutpointr(temp, x, y, use_midpoints = TRUE, pos_class = 1) expect_equal(optcut$optimal_cutpoint, 2.5) expect_warning(plot(optcut)) optcut <- cutpointr(temp, x, y, use_midpoints = TRUE, pos_class = 0) expect_equal(optcut$optimal_cutpoint, 2.5) }) test_that("find_metric_name finds metric", { set.seed(123) tempdat <- data.frame(x = runif(100), y = factor(sample(0:1, size = 100, replace = TRUE))) optcut <- cutpointr(tempdat, x, y, method = maximize_metric, metric = youden) expect_equal(cutpointr:::find_metric_name(optcut), "youden") set.seed(1234) tempdat <- data.frame(x = runif(100), y = factor(sample(0:1, size = 100, replace = TRUE)), g = factor(sample(0:1, size = 100, replace = TRUE))) optcut <- cutpointr(tempdat, x, y, g, method = maximize_metric, metric = youden) expect_equal(cutpointr:::find_metric_name(optcut), "youden") }) test_that("no duplicate column names are returned", { set.seed(123) tempdat <- data.frame(x = runif(100), y = factor(sample(0:1, size = 100, replace = TRUE))) optcut <- cutpointr(tempdat, x, y, method = oc_youden_normal) expect_true(all(table(colnames(optcut)) == 1)) test_plot.cutpointr(optcut) test_ggplot_functions(optcut, do_plot_metric = FALSE) if (require(fANCOVA)) { optcut <- cutpointr(tempdat, x, y, method = oc_youden_kernel) expect_true(all(table(colnames(optcut)) == 1)) expect_silent(plot(optcut)) } optcut <- cutpointr(tempdat, x, y) expect_true(all(table(colnames(optcut)) == 1)) test_plot.cutpointr(optcut) test_ggplot_functions(optcut) optcut <- cutpointr(tempdat, x, y, method = oc_manual, cutpoint = 30) expect_true(all(table(colnames(optcut)) == 1)) test_plot.cutpointr(optcut) test_ggplot_functions(optcut, do_plot_metric = FALSE) set.seed(1234) tempdat <- data.frame(x = rnorm(100), y = factor(sample(0:1, size = 100, replace = TRUE)), g = factor(sample(0:1, size = 100, replace = TRUE))) optcut <- cutpointr(tempdat, x, y, g, method = oc_youden_normal) expect_true(all(table(colnames(optcut)) == 1)) test_plot.cutpointr(optcut) test_ggplot_functions(optcut, do_plot_metric = FALSE) if (require(fANCOVA)) { optcut <- cutpointr(tempdat, x, y, g, method = oc_youden_kernel) expect_true(all(table(colnames(optcut)) == 1)) test_plot.cutpointr(optcut) test_ggplot_functions(optcut, do_plot_metric = FALSE) } optcut <- cutpointr(tempdat, x, y, g) expect_true(all(table(colnames(optcut)) == 1)) test_plot.cutpointr(optcut) test_ggplot_functions(optcut, do_plot_metric = FALSE) optcut <- cutpointr(tempdat, x, y, g, method = oc_manual, cutpoint = 30) expect_true(all(table(colnames(optcut)) == 1)) test_plot.cutpointr(optcut) test_ggplot_functions(optcut, do_plot_metric = FALSE) }) test_that("Correct cutpoints with example data", { exdat <- data.frame(obs = c(0, 0, 1, 1), preds = c(0, 0, 1, 1)) optcut <- cutpointr(exdat, preds, obs, method = minimize_metric, metric = abs_d_sens_spec) expect_equal(optcut$optimal_cutpoint, 1) optcut <- cutpointr(exdat, preds, obs, method = maximize_metric, metric = accuracy) expect_equal(optcut$optimal_cutpoint, 1) optcut <- cutpointr(exdat, preds, obs, method = maximize_metric, metric = youden) expect_equal(optcut$optimal_cutpoint, 1) # With NA exdat <- data.frame(obs = c(NA, 0, 0, 1, 1), preds = c(1, 0, 0, 1, 1)) expect_error(cutpointr(exdat, preds, obs, method = minimize_metric, metric = abs_d_sens_spec)) optcut <- cutpointr(exdat, preds, obs, method = minimize_metric, metric = abs_d_sens_spec, na.rm = T) expect_equal(optcut$optimal_cutpoint, 1) test_plot.cutpointr(optcut) test_ggplot_functions(optcut) optcut <- cutpointr(exdat, preds, obs, method = maximize_metric, metric = accuracy, na.rm = T) expect_equal(optcut$optimal_cutpoint, 1) test_plot.cutpointr(optcut) test_ggplot_functions(optcut) optcut <- cutpointr(exdat, preds, obs, method = maximize_metric, metric = youden, na.rm = T) expect_equal(optcut$optimal_cutpoint, 1) test_plot.cutpointr(optcut) test_ggplot_functions(optcut) # With Inf and -Inf exdat <- data.frame(obs = c(rep(0, 20), 1, 1, 1, 1, 0, 0, 0, 0), preds = c(rep(-Inf, 10), rep(Inf, 10), 0, 0, 0, 0, 1, 1, 1, 1)) optcut <- cutpointr(exdat, preds, obs, method = maximize_metric, metric = cutpointr::youden) expect_equal(optcut$optimal_cutpoint, 1) expect_equal(as.numeric(optcut$specificity), 1) expect_equal(round(as.numeric(optcut$sensitivity), 2), 0.58) }) test_that("Metric colnames that are already in cutpointr are modified", { metricfunc <- function(tp, fp, tn, fn, ...) { res <- matrix(1:length(tp), ncol = 1) colnames(res) <- "sensitivity" return(res) } opt_cut <- cutpointr(suicide, dsi, suicide, metric = metricfunc, boot_runs = 5) expect_equal(colnames(opt_cut)[4], "metric_sensitivity") # test_plot.cutpointr(opt_cut) # test_ggplot_functions(opt_cut) expect_silent(summary(opt_cut)) opt_cut <- cutpointr(suicide, dsi, suicide, gender, metric = metricfunc, boot_runs = 5) expect_equal(colnames(opt_cut)[5], "metric_sensitivity") # test_plot.cutpointr(opt_cut) # test_ggplot_functions(opt_cut) expect_silent(summary(opt_cut)) metricfunc <- function(tp, fp, tn, fn, ...) { res <- matrix(1:length(tp), ncol = 1) colnames(res) <- "AUC" return(res) } opt_cut <- cutpointr(suicide, dsi, suicide, metric = metricfunc, boot_runs = 5) expect_equal(colnames(opt_cut)[4], "metric_AUC") # test_plot.cutpointr(opt_cut) # test_ggplot_functions(opt_cut) expect_silent(summary(opt_cut)) opt_cut <- cutpointr(suicide, dsi, suicide, gender, metric = metricfunc, boot_runs = 5) expect_equal(colnames(opt_cut)[5], "metric_AUC") # test_plot.cutpointr(opt_cut) # test_ggplot_functions(opt_cut) expect_silent(summary(opt_cut)) metricfunc <- function(tp, fp, tn, fn, ...) { res <- matrix(1:length(tp), ncol = 1) colnames(res) <- "roc_curve" return(res) } expect_error(cutpointr(suicide, dsi, suicide, metric = metricfunc, boot_runs = 5)) expect_error(cutpointr(suicide, dsi, suicide, gender, metric = metricfunc, boot_runs = 5)) opt_cut <- cutpointr(suicide, dsi, suicide, method = oc_youden_normal, metric = metricfunc, boot_runs = 5) expect_equal(colnames(opt_cut)[4], "metric_roc_curve") # test_plot.cutpointr(opt_cut) # test_ggplot_functions(opt_cut, do_plot_metric = FALSE) expect_silent(summary(opt_cut)) opt_cut <- cutpointr(suicide, dsi, suicide, gender, method = oc_youden_normal, metric = metricfunc, boot_runs = 5) expect_equal(colnames(opt_cut)[5], "metric_roc_curve") # test_plot.cutpointr(opt_cut) # test_ggplot_functions(opt_cut, do_plot_metric = FALSE) expect_silent(summary(opt_cut)) }) test_that("SE and NSE interface give identical results", { opt_cut_nse <- cutpointr(suicide, dsi, suicide) opt_cut_se <- suppressWarnings(cutpointr_(suicide, "dsi", "suicide")) expect_identical(opt_cut_se, opt_cut_nse) opt_cut_nse <- cutpointr(suicide, dsi, suicide) opt_cut_se <- suppressWarnings(cutpointr_(suicide, "dsi", "suicide", method = maximize_metric)) expect_identical(opt_cut_se, opt_cut_nse) opt_cut_nse <- cutpointr(suicide, dsi, suicide, gender) opt_cut_se <- suppressWarnings(cutpointr_(suicide, "dsi", "suicide", "gender")) expect_identical(opt_cut_se, opt_cut_nse) opt_cut_nse <- cutpointr(suicide, dsi, suicide, gender) opt_cut_se <- suppressWarnings(cutpointr_(suicide, "dsi", "suicide", "gender")) expect_identical(opt_cut_se, opt_cut_nse) }) test_that("cutpointr detects wrong number of classes", { tempdat <- data.frame(cl = factor(c("a", "b", "c")), x = 1:3) expect_error(cutpointr(tempdat, x, cl)) expect_error(cutpointr(tempdat, x, cl, pos_class = "a", neg_class = "b", direction = ">=")) tempdat <- data.frame(cl = factor(c("a", "a", "a")), x = 1:3) expect_error(cutpointr(tempdat, x, cl)) expect_error(cutpointr(tempdat, x, cl, pos_class = "a", neg_class = "b", direction = ">=")) }) test_that("Bootstrap returns plausible results", { set.seed(123) opt_cut <- suppressWarnings(cutpointr(suicide, dsi, suicide, boot_runs = 50, break_ties = mean)) expect_true(mean(opt_cut$boot[[1]]$sum_sens_spec_b) > 1.3 & mean(opt_cut$boot[[1]]$sum_sens_spec_b) < 3) expect_true(sd(opt_cut$boot[[1]]$sum_sens_spec_b) > 0.02 & sd(opt_cut$boot[[1]]$sum_sens_spec_b) < 1) expect_true(mean(opt_cut$boot[[1]]$sum_sens_spec_oob) > 1.3 & mean(opt_cut$boot[[1]]$sum_sens_spec_oob) < 3) expect_true(sd(opt_cut$boot[[1]]$sum_sens_spec_oob) > 0.02 & sd(opt_cut$boot[[1]]$sum_sens_spec_oob) < 1) set.seed(123) opt_cut <- suppressWarnings(cutpointr(suicide, dsi, suicide, boot_runs = 30, direction = "<=")) expect_true(mean(opt_cut$boot[[1]]$sum_sens_spec_b) > 1.3 & mean(opt_cut$boot[[1]]$sum_sens_spec_b) < 3) expect_true(sd(opt_cut$boot[[1]]$sum_sens_spec_b) > 0.02 & sd(opt_cut$boot[[1]]$sum_sens_spec_b) < 1) expect_true(mean(opt_cut$boot[[1]]$sum_sens_spec_oob) > 1.3 & mean(opt_cut$boot[[1]]$sum_sens_spec_oob) < 3) expect_true(sd(opt_cut$boot[[1]]$sum_sens_spec_oob) > 0.02 & sd(opt_cut$boot[[1]]$sum_sens_spec_oob) < 1) set.seed(123) opt_cut <- suppressWarnings(cutpointr(suicide, dsi, suicide, boot_runs = 30, pos_class = "no")) expect_true(mean(opt_cut$boot[[1]]$sum_sens_spec_b) > 1.3 & mean(opt_cut$boot[[1]]$sum_sens_spec_b) < 3) expect_true(sd(opt_cut$boot[[1]]$sum_sens_spec_b) > 0.02 & sd(opt_cut$boot[[1]]$sum_sens_spec_b) < 1) expect_true(mean(opt_cut$boot[[1]]$sum_sens_spec_oob) > 1.3 & mean(opt_cut$boot[[1]]$sum_sens_spec_oob) < 3) expect_true(sd(opt_cut$boot[[1]]$sum_sens_spec_oob) > 0.02 & sd(opt_cut$boot[[1]]$sum_sens_spec_oob) < 1) }) test_that("Summary by class returns correct stats", { digits <- 3 # No subgroup, no NA oc <- cutpointr(suicide, dsi, suicide) s <- summary(oc) my <- mean(suicide[suicide$suicide == "yes", "dsi"]) expect_equal(s$desc_by_class[[1]][2, "Mean"], my, tolerance = 1e-3) mn <- mean(suicide[suicide$suicide == "no", "dsi"]) expect_equal(s$desc_by_class[[1]][1, "Mean"], mn) # No subgroup, with NA tempdat <- suicide tempdat[10, 1] <- NA tempdat[20, 2] <- NA tempdat[30, 3] <- NA tempdat[40, 4] <- NA oc <- cutpointr(tempdat, dsi, suicide, na.rm = TRUE) s <- summary(oc) my <- mean(tempdat[tempdat$suicide == "yes", "dsi"], na.rm = TRUE) expect_equal(s$desc_by_class[[1]][2, "Mean"], my, tolerance = 1e-3) mn <- mean(tempdat[tempdat$suicide == "no", "dsi"], na.rm = TRUE) expect_equal(s$desc_by_class[[1]][1, "Mean"], mn) # With subgroup, no NA oc <- cutpointr(suicide, dsi, suicide, gender) s <- summary(oc) myf <- mean(suicide[suicide$suicide == "yes" & suicide$gender == "female", "dsi"]) expect_equal(s$desc_by_class[[1]][2, "Mean"], myf, tolerance = 1e-3) mnf <- mean(suicide[suicide$suicide == "no" & suicide$gender == "female", "dsi"]) expect_equal(s$desc_by_class[[1]][1, "Mean"], mnf) mym <- mean(suicide[suicide$suicide == "yes" & suicide$gender == "male", "dsi"]) expect_equal(s$desc_by_class[[2]][2, "Mean"], mym, tolerance = 1e-3) mnm <- mean(suicide[suicide$suicide == "no" & suicide$gender == "male", "dsi"]) expect_equal(s$desc_by_class[[2]][1, "Mean"], mnm) # With subgroup, with NA tempdat <- suicide tempdat[10, 1] <- NA tempdat[20, 2] <- NA tempdat[30, 3] <- NA tempdat[40, 4] <- NA oc <- cutpointr(tempdat, dsi, suicide, gender, na.rm = TRUE) s <- summary(oc) myf <- mean(tempdat[tempdat$suicide == "yes" & tempdat$gender == "female", "dsi"], na.rm = TRUE) expect_equal(s$desc_by_class[[1]][2, "Mean"], myf, tolerance = 1e-3) mnf <- mean(tempdat[tempdat$suicide == "no" & tempdat$gender == "female", "dsi"], na.rm = TRUE) expect_equal(s$desc_by_class[[1]][1, "Mean"], mnf) mym <- mean(tempdat[tempdat$suicide == "yes" & tempdat$gender == "male", "dsi"], na.rm = TRUE) expect_equal(s$desc_by_class[[2]][2, "Mean"], mym, tolerance = 1e-3) mnm <- mean(tempdat[tempdat$suicide == "no" & tempdat$gender == "male", "dsi"], na.rm = TRUE) expect_equal(s$desc_by_class[[2]][1, "Mean"], mnm) }) test_that("Results for youden are correct", { opt_cut_cp <- cutpointr(suicide, dsi, suicide, metric = youden) expect_equal(opt_cut_cp$optimal_cutpoint, 2) opt_cut_cp <- cutpointr(suicide, dsi, suicide, gender, metric = youden) expect_equal(opt_cut_cp$optimal_cutpoint, c(2, 3)) }) test_that("Results for p_chisquared are equal to results by OptimalCutpoints", { suppressWarnings(RNGversion("3.5.0")) set.seed(2839) tempdat <- data.frame(x = c(rnorm(50), rnorm(50, mean = 1)) , y = c(rep(0, 50), rep(1, 50)), group = sample(c("a", "b"), size = 100, replace = TRUE)) suppressWarnings( opt_cut_cp <- cutpointr(tempdat, x, y, method = minimize_metric, metric = p_chisquared, direction = ">=", pos_class = 1, tol_metric = 0) ) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 0.9335) suppressWarnings( opt_cut_cp <- cutpointr(tempdat, x, y, group, method = minimize_metric, metric = p_chisquared, direction = ">=", pos_class = 1, tol_metric = 0) ) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), c(0.9335, 0.5676)) }) test_that("Results for prod_sens_spec are equal to results by OptimalCutpoints", { set.seed(839) tempdat <- data.frame(x = c(rnorm(50), rnorm(50, mean = 1)) , y = c(rep(0, 50), rep(1, 50)), group = sample(c("a", "b"), size = 100, replace = TRUE)) opt_cut_cp <- cutpointr(tempdat, x, y, method = maximize_metric, metric = prod_sens_spec, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 0.7783) opt_cut_cp <- cutpointr(tempdat, x, y, group, method = maximize_metric, metric = prod_sens_spec, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), c(0.7998, 0.6371)) }) test_that("Results for abs_d_ppv_npv are equal to results by OptimalCutpoints", { set.seed(389) tempdat <- data.frame(x = c(rnorm(50), rnorm(50, mean = 1)) , y = c(rep(0, 50), rep(1, 50)), group = sample(c("a", "b"), size = 100, replace = TRUE)) opt_cut_cp <- cutpointr(tempdat, x, y, method = minimize_metric, metric = abs_d_ppv_npv, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 0.5677) opt_cut_cp <- cutpointr(tempdat, x, y, group, method = minimize_metric, metric = abs_d_ppv_npv, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), c(0.2501, 0.6781)) }) test_that("Results for sum_ppv_npv are equal to results by OptimalCutpoints", { set.seed(389) tempdat <- data.frame(x = c(rnorm(50), rnorm(50, mean = 1)) , y = c(rep(0, 50), rep(1, 50)), group = sample(c("a", "b"), size = 100, replace = TRUE)) opt_cut_cp <- cutpointr(tempdat, x, y, method = maximize_metric, metric = sum_ppv_npv, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 1.7835) opt_cut_cp <- cutpointr(tempdat, x, y, group, method = maximize_metric, metric = sum_ppv_npv, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), c(-0.7339, 1.7835)) }) test_that("Results for prod_ppv_npv are equal to results by OptimalCutpoints", { set.seed(389) tempdat <- data.frame(x = c(rnorm(50), rnorm(50, mean = 1)) , y = c(rep(0, 50), rep(1, 50)), group = sample(c("a", "b"), size = 100, replace = TRUE)) opt_cut_cp <- cutpointr(tempdat, x, y, method = maximize_metric, metric = prod_ppv_npv, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 1.7835) opt_cut_cp <- cutpointr(tempdat, x, y, group, method = maximize_metric, metric = prod_ppv_npv, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), c(-0.7339, 1.7835)) }) test_that("Results for accuracy are equal to results by OptimalCutpoints", { set.seed(38429) tempdat <- data.frame(x = c(rnorm(100), rnorm(100, mean = 1)) , y = c(rep(0, 100), rep(1, 100)), group = sample(c("a", "b"), size = 200, replace = TRUE)) opt_cut_cp <- cutpointr(tempdat, x, y, method = maximize_metric, metric = accuracy, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 0.4312) opt_cut_cp <- cutpointr(tempdat, x, y, group, method = maximize_metric, metric = accuracy, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), c(0.9771, 0.0744)) }) test_that("Results for roc01 are equal to results by OptimalCutpoints", { set.seed(1957) tempdat <- data.frame(x = c(rnorm(100), rnorm(100, mean = 1)) , y = c(rep(0, 100), rep(1, 100)), group = sample(c("a", "b"), size = 200, replace = TRUE)) opt_cut_cp <- cutpointr(tempdat, x, y, method = minimize_metric, metric = roc01, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 0.3255) opt_cut_cp <- cutpointr(tempdat, x, y, group, method = minimize_metric, metric = roc01, direction = ">=", pos_class = 1) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), c(0.5312, 0.3307)) }) test_that("Results for constrained metrics are equal to results by OptimalCutpoints", { set.seed(38129) tempdat <- data.frame(x = c(rnorm(100), rnorm(100, mean = 1)) , y = c(rep(0, 100), rep(1, 100)), group = sample(c("a", "b"), size = 200, replace = TRUE)) opt_cut_cp <- cutpointr(tempdat, x, y, metric = sens_constrain, min_constrain = 0.85, constrain_metric = specificity) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 1.3018) expect_equal(round(opt_cut_cp$sens_constrain, 4), 0.44) expect_equal(round(as.numeric(opt_cut_cp$specificity), 4), 0.8500) expect_equal(as.numeric(opt_cut_cp$sensitivity), opt_cut_cp$sens_constrain) opt_cut_cp <- cutpointr(tempdat, x, y, metric = spec_constrain, min_constrain = 0.85, constrain_metric = sensitivity) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 0.2775) expect_equal(round(opt_cut_cp$spec_constrain, 4), 0.54) expect_equal(round(as.numeric(opt_cut_cp$sensitivity), 4), 0.8500) expect_equal(as.numeric(opt_cut_cp$specificity), opt_cut_cp$spec_constrain) opt_cut_cp <- cutpointr(tempdat, x, y, metric = metric_constrain, min_constrain = 0.85, constrain_metric = npv, main_metric = ppv) %>% add_metric(list(npv, ppv)) expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), 0.1435) expect_equal(round(opt_cut_cp$ppv_constrain, 4), 0.6500) expect_equal(round(opt_cut_cp$npv, 4), 0.8500) expect_equal(opt_cut_cp$ppv_constrain, opt_cut_cp$ppv) }) test_that("Results for F1_score are equal to results by ROCR", { set.seed(38429) tempdat <- data.frame(x = c(rnorm(100), rnorm(100, mean = 1)) , y = c(rep(0, 100), rep(1, 100)), group = sample(c("a", "b"), size = 200, replace = TRUE)) f1_cp <- cutpointr(tempdat, x, y, method = maximize_metric, metric = F1_score, direction = ">=", pos_class = 1) # rocr_pred <- ROCR::prediction(tempdat$x, tempdat$y) # f1_rocr <- ROCR::performance(rocr_pred, "f") # f1_rocr_yvals <- round(f1_rocr@y.values[[1]], 4) f1_rocr_yvals <- c(NaN, 0.0198, 0.0392, 0.0583, 0.0769, 0.0952, 0.1132, 0.1308, 0.1481, 0.1651, 0.1818, 0.1802, 0.1964, 0.2124, 0.2281, 0.2435, 0.2586, 0.2735, 0.2881, 0.3025, 0.3167, 0.3140, 0.3279, 0.3252, 0.3387, 0.3520, 0.3651, 0.3780, 0.3906, 0.3876, 0.4000, 0.4122, 0.4242, 0.4211, 0.4328, 0.4444, 0.4412, 0.4526, 0.4493, 0.4604, 0.4714, 0.4823, 0.4789, 0.4895, 0.5000, 0.5103, 0.5205, 0.5306, 0.5405, 0.5503, 0.5600, 0.5563, 0.5658, 0.5621, 0.5714, 0.5806, 0.5897, 0.5860, 0.5949, 0.6038, 0.6000, 0.5963, 0.6049, 0.6135, 0.6098, 0.6182, 0.6145, 0.6228, 0.6310, 0.6391, 0.6353, 0.6433, 0.6395, 0.6474, 0.6437, 0.6400, 0.6364, 0.6441, 0.6404, 0.6480, 0.6444, 0.6409, 0.6374, 0.6448, 0.6522, 0.6486, 0.6559, 0.6524, 0.6489, 0.6561, 0.6526, 0.6597, 0.6667, 0.6736, 0.6804, 0.6872, 0.6837, 0.6802, 0.6869, 0.6935, 0.7000, 0.6965, 0.7030, 0.7094, 0.7059, 0.7024, 0.6990, 0.7053, 0.7019, 0.6986, 0.6952, 0.7014, 0.7075, 0.7136, 0.7103, 0.7163, 0.7130, 0.7097, 0.7156, 0.7123, 0.7091, 0.7149, 0.7207, 0.7175, 0.7232, 0.7289, 0.7257, 0.7225, 0.7193, 0.7162, 0.7130, 0.7100, 0.7069, 0.7124, 0.7179, 0.7234, 0.7288, 0.7257, 0.7227, 0.7197, 0.7250, 0.7220, 0.7190, 0.7160, 0.7131, 0.7184, 0.7154, 0.7126, 0.7097, 0.7149, 0.7120, 0.7092, 0.7063, 0.7115, 0.7087, 0.7059, 0.7109, 0.7082, 0.7132, 0.7104, 0.7077, 0.7050, 0.7023, 0.6996, 0.6970, 0.7019, 0.6992, 0.7041, 0.7015, 0.7063, 0.7037, 0.7011, 0.7059, 0.7033, 0.7007, 0.6982, 0.6957, 0.6931, 0.6978, 0.6953, 0.6929, 0.6904, 0.6879, 0.6855, 0.6901, 0.6877, 0.6853, 0.6829, 0.6875, 0.6851, 0.6828, 0.6804, 0.6781, 0.6758, 0.6735, 0.6780, 0.6757, 0.6734, 0.6711, 0.6689, 0.6667) expect_identical(f1_rocr_yvals[-1], round(f1_cp$roc_curve[[1]]$m, 4)[-1]) }) test_that("Results for misclassification_cost are equal to results by OptimalCutpoints", { set.seed(429) tempdat <- data.frame(x = c(rnorm(100), rnorm(100, mean = 1)) , y = c(rep(0, 100), rep(1, 100)), group = sample(c("a", "b"), size = 200, replace = TRUE)) opt_cut_cp <- cutpointr(tempdat, x, y, method = minimize_metric, metric = misclassification_cost, direction = ">=", cost_fp = 1, cost_fn = 3, pos_class = 1) # oc_cont <- control.cutpoints(CFP = 1, CFN = 3) # opt_cut_oc <- OptimalCutpoints::optimal.cutpoints(X = "x", status = "y", # method = "MCT", # tag.healthy = 0, # direction = "<", # data = tempdat, # control = oc_cont) # opt_cut_oc <- opt_cut_oc$MCT$Global$optimal.cutoff$cutoff expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), -0.2973) opt_cut_cp <- cutpointr(tempdat, x, y, group, method = minimize_metric, metric = misclassification_cost, direction = ">=", cost_fp = 1, cost_fn = 3, pos_class = 1) # oc_cont <- control.cutpoints(CFP = 1, CFN = 3) # opt_cut_oc <- OptimalCutpoints::optimal.cutpoints(X = "x", status = "y", # categorical.cov = "group", # method = "MCT", # tag.healthy = 0, # direction = "<", # data = tempdat, # control = oc_cont) # opt_cut_oc_a <- opt_cut_oc$MCT$a$optimal.cutoff$cutoff # opt_cut_oc_b <- opt_cut_oc$MCT$b$optimal.cutoff$cutoff expect_equal(round(opt_cut_cp$optimal_cutpoint, 4), c(-0.7498, -0.0824)) }) test_that("LOESS smoothing does not return warnings or errors", { set.seed(38429) tempdat <- data.frame(x = c(rnorm(100), rnorm(100, mean = 1)) , y = c(rep(0, 100), rep(1, 100)), group = sample(c("a", "b"), size = 200, replace = TRUE)) expect_silent( suppressMessages( cp <- cutpointr(tempdat, x, y, method = maximize_loess_metric, user.span = 1, metric = accuracy, direction = ">=", pos_class = 1, boot_runs = 10) ) ) expect_equal(round(cp$optimal_cutpoint, 3), 0.507) expect_equal(round(cp$loess_accuracy, 3), 0.690) set.seed(208) tempdat <- data.frame(x = c(rnorm(100), rnorm(100, mean = 2)) , y = c(rep(0, 100), rep(1, 100)), group = sample(c("a", "b"), size = 200, replace = TRUE)) expect_silent( suppressMessages( cp <- cutpointr(tempdat, x, y, method = maximize_loess_metric, metric = accuracy, direction = ">=", pos_class = 1, boot_runs = 10) ) ) expect_silent( suppressMessages( cutpointr(tempdat, x, y, group, method = maximize_loess_metric, user.span = 1, metric = accuracy, direction = ">=", pos_class = 1, boot_runs = 10) ) ) set.seed(3429) tempdat <- data.frame(x = c(rnorm(100), rnorm(100, mean = 1)) , y = c(rep(0, 100), rep(1, 100)), group = sample(c("a", "b"), size = 200, replace = TRUE)) expect_silent( suppressMessages( cp <- cutpointr(tempdat, x, y, method = minimize_loess_metric, user.span = 1, break_ties = mean, metric = abs_d_ppv_npv, direction = ">=", pos_class = 1, boot_runs = 10) ) ) expect_equal(round(cp$optimal_cutpoint, 3), -0.083) expect_equal(round(cp$loess_abs_d_ppv_npv, 3), 0.156) expect_silent( cp <- cutpointr(tempdat, x, y, group, method = minimize_loess_metric, user.span = 1, break_ties = mean, silent = TRUE, metric = abs_d_ppv_npv, direction = ">=", pos_class = 1, boot_runs = 100) ) expect_equal(round(cp$optimal_cutpoint, 2), c(-1.29, 1.01)) }) test_that("cutpointr returns same result with NSE interface and raw data", { oc1 <- cutpointr(suicide, dsi, suicide, metric = prod_sens_spec) oc2 <- cutpointr(x = suicide$dsi, class = suicide$suicide, metric = prod_sens_spec) expect_true(oc1$optimal_cutpoint == 2) expect_true(oc2$optimal_cutpoint == 2) expect_true(oc1$prod_sens_spec == oc2$prod_sens_spec) oc1 <- cutpointr(suicide, dsi, suicide, gender, metric = prod_sens_spec) oc2 <- cutpointr(x = suicide$dsi, class = suicide$suicide, subgroup = suicide$gender, metric = prod_sens_spec) expect_true(all(oc1$prod_sens_spec == oc2$prod_sens_spec)) expect_true(all(oc1$optimal_cutpoint == oc2$optimal_cutpoint)) }) test_that("Prevalence is correctly calculated", { tempdat <- data.frame(x = 1:100, y = c(rep(0, 10), rep(1, 90))) oc <- cutpointr(tempdat, x, y, pos_class = 1, direction = ">=") expect_equal(oc$prevalence, 0.9) tempdat <- data.frame(x = 100:1, y = c(rep(0, 10), rep(1, 90))) oc <- cutpointr(tempdat, x, y, pos_class = 1, direction = ">=") expect_equal(oc$prevalence, 0.9) tempdat <- data.frame(x = 1:100, y = c(rep(0, 10), rep(1, 90))) oc <- cutpointr(tempdat, x, y, pos_class = 1, direction = "<=") expect_equal(oc$prevalence, 0.9) tempdat <- data.frame(x = 100:1, y = c(rep(0, 10), rep(1, 90))) oc <- cutpointr(tempdat, x, y, pos_class = 1, direction = "<=") expect_equal(oc$prevalence, 0.9) }) test_that("multi_cutpointr runs without errors", { mc <- multi_cutpointr(suicide, x = c("age", "dsi"), class = "suicide", pos_class = "yes") expect_equal(mc$optimal_cutpoint, c(55, 2)) mc <- multi_cutpointr(suicide, x = c("age", "dsi"), class = "suicide", subgroup = NULL, pos_class = "yes") expect_equal(mc$optimal_cutpoint, c(55, 2)) mc2 <- multi_cutpointr(suicide, class = "suicide", pos_class = "yes") expect_identical(mc, mc2) mc3 <- multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide, subgroup = NULL, pos_class = "yes") expect_identical(mc, mc3) mcg <- multi_cutpointr(suicide, x = c("age", "dsi"), class = "suicide", subgroup = "gender", pos_class = "yes") expect_equal(mcg$optimal_cutpoint, c(55, 21, 2, 3)) mcg2 <- multi_cutpointr(suicide, class = "suicide", subgroup = "gender", pos_class = "yes") expect_identical(mcg, mcg2) mcg3 <- multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide, subgroup = gender, pos_class = "yes") expect_identical(mcg, mcg3) mcg4 <- multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide, subgroup = "gender", pos_class = "yes") expect_identical(mcg, mcg4) my_group_var <- "gender" my_class_var <- "suicide" mcg_new <- multi_cutpointr(suicide, x = c("age", "dsi"), class = !!my_class_var, subgroup = !!my_group_var, pos_class = "yes") expect_identical(mcg, mcg_new) mcg_new <- multi_cutpointr(suicide, x = c("age", "dsi"), class = !!my_class_var, subgroup = "gender", pos_class = "yes") expect_identical(mcg, mcg_new) # We had a bug with cutpointr 1.0.32 where class had to be named suicide temp <- suicide temp$foo <- temp$suicide temp$suicide <- NULL temp_mc <- multi_cutpointr(temp, x = c("age", "dsi"), class = foo) expect_equal(temp_mc$optimal_cutpoint, c(56, 2)) }) test_that("AUC is always >= 0.5 with automatic assumptions", { tempdat <- data.frame(x = c(5:1, 100), y = c(2,2,2,1,1,1)) oc <- cutpointr(tempdat, x, y) expect_true(oc$AUC >= 0.5) oc <- cutpointr(tempdat, x, y, pos_class = 2) expect_true(oc$AUC >= 0.5) }) test_that("find_metric_name_boot finds correct metric", { oc <- cutpointr(x = suicide$dsi, class = suicide$suicide, metric = abs_d_sens_spec, method = minimize_metric, boot_runs = 3) expect_true(cutpointr:::find_metric_name_boot(oc$boot[[1]]) == "abs_d_sens_spec_oob") oc <- cutpointr(x = suicide$dsi, class = suicide$suicide, subgroup = suicide$gender, metric = abs_d_sens_spec, method = minimize_metric, boot_runs = 3) expect_true(cutpointr:::find_metric_name_boot(oc$boot[[1]]) == "abs_d_sens_spec_oob") }) test_that("error if NSE and raw interface are mixed", { expect_error(cutpointr(suicide, suicide$dsi, suicide$suicide)) expect_error(cutpointr(suicide, suicide$dsi, suicide)) expect_error(cutpointr(suicide, dsi, suicide$suicide)) expect_error(cutpointr(suicide, dsi, suicide, suicide$gender)) }) test_that("error if metric/method is not a function", { expect_error(cutpointr(suicide, dsi, suicide, metric = "accuracy")) expect_error(cutpointr(suicide, dsi, suicide, method = "oc_youden_kernel")) }) test_that("same results with or without silent", { expect_identical( cutpointr(suicide, dsi, suicide, gender), cutpointr(suicide, dsi, suicide, gender, silent = TRUE) ) }) test_that("plot_cutpointr runs", { oc <- cutpointr(suicide, dsi, suicide) expect_is(plot_cutpointr(oc), "ggplot") expect_is(plot_cutpointr(oc, cutpoints, fpr), "ggplot") expect_is(plot_cutpointr(oc, cutpoints, function(tp, fp, ...) tp + fp), "ggplot") oc <- cutpointr(suicide, dsi, suicide, gender) expect_is(plot_cutpointr(oc), "ggplot") expect_is(plot_cutpointr(oc, cutpoints, fpr), "ggplot") expect_is(plot_cutpointr(oc, cutpoints, function(tp, fp, ...) tp + fp), "ggplot") set.seed(100) oc <- cutpointr(suicide, dsi, suicide, boot_runs = 5) expect_is(plot_cutpointr(oc), "ggplot") expect_is(plot_cutpointr(oc, cutpoints, fpr), "ggplot") expect_is(plot_cutpointr(oc, cutpoints, function(tp, fp, ...) tp + fp), "ggplot") set.seed(100) oc <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 5) expect_is(plot_cutpointr(oc), "ggplot") expect_is(plot_cutpointr(oc, cutpoints, fpr), "ggplot") expect_is(plot_cutpointr(oc, cutpoints, function(tp, fp, ...) tp + fp), "ggplot") set.seed(100) expect_warning(plot_cutpointr( cutpointr(suicide, dsi, suicide, boot_runs = 5), fpr, tpr )) set.seed(200) expect_warning(plot_cutpointr( cutpointr(suicide, dsi, suicide, gender, boot_runs = 5), fpr, tpr )) }) test_that("gam smoothing leads to plausible results", { cp <- cutpointr(suicide, dsi, suicide, method = maximize_gam_metric, metric = youden) expect_equal(cp$optimal_cutpoint, 2) test_plot.cutpointr(cp) test_ggplot_functions(cp) expect_silent(summary(cp)) cp <- cutpointr(suicide, dsi, suicide, gender, method = maximize_gam_metric, metric = youden) expect_equal(cp$optimal_cutpoint, c(2, 2)) test_plot.cutpointr(cp) test_ggplot_functions(cp) expect_silent(summary(cp)) cp <- cutpointr(suicide, dsi, suicide, gender, method = minimize_gam_metric, metric = abs_d_sens_spec) expect_equal(cp$optimal_cutpoint, c(2, 2)) test_plot.cutpointr(cp) test_ggplot_functions(cp) expect_silent(summary(cp)) }) test_that("bootstrapped cutpoints lead to plausible results", { set.seed(914) cp <- cutpointr(suicide, dsi, suicide, method = maximize_boot_metric, metric = youden, boot_cut = 10) expect_equal(cp$optimal_cutpoint, 1.9) test_plot.cutpointr(cp) test_ggplot_functions(cp, do_plot_metric = FALSE) expect_silent(summary(cp)) set.seed(14) cp <- cutpointr(suicide, dsi, suicide, gender, method = maximize_boot_metric, metric = youden, boot_cut = 10) expect_equal(cp$optimal_cutpoint, c(2.2, 3.2)) test_plot.cutpointr(cp) test_ggplot_functions(cp, do_plot_metric = FALSE) expect_silent(summary(cp)) set.seed(15) cp <- cutpointr(suicide, dsi, suicide, gender, method = minimize_boot_metric, metric = abs_d_sens_spec, boot_cut = 10) expect_equal(cp$optimal_cutpoint, c(2.1, 2.2)) expect_error(plot_metric(cp)) test_plot.cutpointr(cp) test_ggplot_functions(cp, do_plot_metric = FALSE) expect_silent(summary(cp)) }) test_that("cutpointr handles multiple optimal cutpoints correctly", { tempdat <- data.frame(y = c(0,0,0,1,0,1,1,1), x = 1:8) expect_message(cp <- cutpointr(tempdat, x = x, class = y, break_ties = c, pos_class = 1, direction = ">=")) expect_equal(cp$optimal_cutpoint[[1]], c(6, 4)) expect_message(cp <- cutpointr(tempdat, x = x, class = y, break_ties = c, use_midpoints = TRUE, pos_class = 1, direction = ">=")) expect_equal(cp$optimal_cutpoint[[1]], c(5.5, 3.5)) tempdat_g <- data.frame(g = c(rep(1, 8), rep(2, 8)), y = c(tempdat$y, tempdat$y), x = c(tempdat$x, tempdat$x + 2)) expect_message(cp <- cutpointr(tempdat_g, x = x, class = y, pos_class = 1, direction = ">=", subgroup = g, break_ties = c)) preds <- predict(object = cp, newdata = data.frame(x = c(3,6,7,1), g = c(1,2,2,1)), cutpoint_nr = 2) expect_equal(preds, c(0,1,1,0)) preds <- predict(object = cp, newdata = data.frame(x = c(3,4,5,8), g = c(1,1,2,2)), cutpoint_nr = c(2, 1)) expect_equal(preds, c(0,1,0,1)) # 5 "optimal" cutpoints at 1.2. Only one is returned without # using a tolerance argument due to floating point problems dat <- structure(list(x = c(107.163316194991, 105.577309820546, 114.819340158769, 93.8701224510515, 111.161366154904, 110.365480099412, 98.3751686809715, 90.2407330717812, 89.1085480911548, 104.57786957945, 99.2887326627911, 117.791026668514, 105.351379604962, 96.280551248365, 89.7445775150289, 100.175983253947, 109.428883928973, 101.490653529433, 111.142301202307, 102.656619478302, 104.944400912055, 98.6949032719217, 125.050435849087, 109.326217314704, 108.306336404995, 89.0813758511593, 112.597918995493, 95.7637641112029, 97.084784256457, 115.183411710216), group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("h", "d"), class = "factor")), .Names = c("x", "group"), row.names = c(NA, -30L), class = "data.frame") cp <- cutpointr(dat, x, group, break_ties = c, tol_metric = 1e-6) expect_equal(length(unlist(cp$optimal_cutpoint)), 5) }) test_that("Main metric gets replaced correctly when ties are broken", { dat <- structure(list(x = c(101.805229018197, 107.847340401023, 86.4683542621282, 119.832982062599, 112.384717044928, 112.006173961394, 108.961498842131, 102.536897550745, 105.496003408587, 119.033710510161, 124.445336141903, 111.152957581359, 103.727459196182, 97.3051126961894, 107.721798530394, 107.3951172956, 94.2585671912768, 127.544243110669, 93.2168880188317, 115.311444925151), group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("h", "d"), class = "factor")), .Names = c("x", "group"), row.names = c(NA, -20L), class = "data.frame") cp <- cutpointr(dat, x, group, break_ties = c) cp2 <- cutpointr(dat, x, group, break_ties = mean) expect_equal(unlist(cp$sum_sens_spec), c(1.2, 1.2)) expect_equal(cp2$sum_sens_spec, 1.1) # Different metric cp <- cutpointr(dat, x, group, metric = accuracy, method = maximize_metric, break_ties = c) cp2 <- cutpointr(dat, x, group, metric = accuracy, method = maximize_metric, break_ties = mean) expect_equal(unlist(cp$accuracy), c(0.6, 0.6)) expect_equal(as.numeric(unlist(cp$accuracy)), unlist(cp$acc)) expect_equal(cp2$accuracy, 0.55) expect_equal(as.numeric(cp2$accuracy), as.numeric(cp2$acc)) # With subgroup dat <- structure(list(x = c(112.154869479653, 85.0195562661719, 93.9648809281475, 111.629388719907, 93.6448243724487, 117.357328029692, 98.3663682555138, 105.201729160301, 98.7939462917362, 103.013183787135, 106.178862160569, 108.635928791856, 93.964291812696, 99.9357423611922, 106.763495052307, 114.17384726262, 127.593415952213, 95.1459299909085, 124.619049508866, 103.578770674126, 118.606425125718, 117.882345070528, 113.320440921296, 115.780191821353, 99.4794449460106, 115.080791705234, 104.429126735958, 119.686460888845, 106.942508474107, 119.110377570583, 121.929559539621, 121.503911629438, 116.669635368185, 106.99542786452, 106.089271831433, 119.975572786775, 120.126938685093, 98.1659607850261, 110.128131439393, 108.365631379854), group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("h", "d"), class = "factor"), subgroup = c(0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L)), .Names = c("x", "group", "subgroup" ), row.names = c(NA, -40L), class = "data.frame") cp <- cutpointr(dat, x, group, subgroup, break_ties = c) cp2 <- cutpointr(dat, x, group, subgroup, break_ties = function(x) mean(x) - 10) expect_equal(round(cp$optimal_cutpoint[[2]], 4), c(113.3204, 110.1281)) expect_equal(round(cp$sum_sens_spec[[2]], 3), rep(1.667, 2)) expect_equal(round(cp2$optimal_cutpoint[[2]], 2), 101.72) expect_equal(round(cp2$sum_sens_spec[[2]], 3), 1.222) }) test_that("stratification works", { set.seed(9174) cps <- cutpointr(suicide, dsi, suicide, metric = youden, boot_runs = 30, boot_stratify = TRUE) expect_true(all(cps$boot[[1]]$TP_b + cps$boot[[1]]$FN_b == 36)) set.seed(174) cps <- cutpointr(suicide, dsi, suicide, metric = youden, boot_runs = 30, boot_stratify = FALSE) expect_true(any(cps$boot[[1]]$TP_b + cps$boot[[1]]$FN_b != 36)) # maximize_boot_metric will produce errors without stratification # if one class is missing in the bootstrap sample tempdat <- head(suicide, 130) set.seed(1234) expect_warning( expect_error( cutpointr(tempdat, dsi, suicide, method = maximize_boot_metric, boot_stratify = FALSE, boot_cut = 10) ) ) set.seed(1234) cp <- cutpointr(tempdat, dsi, suicide, method = maximize_boot_metric, boot_stratify = TRUE, boot_cut = 10) expect_identical(cp$optimal_cutpoint, 4.2) }) test_that("cutpointr works with custom method function", { CutOff_Optimised <- function(data, x, class, pos_class, neg_class, direction, ...){ stopifnot(direction == ">=") Obs <- data[[class]] == pos_class Fit <- data[[x]] SumObs <- sum(Obs) LengObs <- length(Obs) tt <- c(0) Cut <- c(0,0,0) if(length(unique(Fit))==1){ Cut[1] <- unique(Fit) Cut[2] <- 100*sum((Fit>=Cut[1])[Obs==1])/SumObs Cut[3] <- 100*sum((Fit=i)[Obs==1])/SumObs sp <- sum((Fit=b)[Obs==1])/SumObs sp <- sum((Fit=") expect_output(print(scp), "Nr. of bootstraps: 10") expect_output(print(scp), "accuracy_oob [0-9]*") set.seed(83) cp <- cutpointr(suicide, dsi, suicide, gender, method = oc_youden_normal, metric = accuracy, boot_runs = 10) scp <- summary(cp) expect_output(print(scp), "Bootstrap summary:") expect_output(print(scp), "Predictor summary:") expect_output(print(scp), "Method: oc_youden_normal") expect_output(print(scp), "Predictor: dsi") expect_output(print(scp), "Subgroup: female") expect_output(print(scp), "Subgroup: male") expect_output(print(scp), "Outcome: suicide") expect_output(print(scp), "Direction: >=") expect_output(print(scp), "Nr. of bootstraps: 10") expect_output(print(scp), "accuracy_oob [0-9]*") set.seed(8213) cp <- cutpointr(suicide$dsi, suicide$suicide, suicide$gender, method = oc_youden_normal, metric = accuracy, boot_runs = 10) scp <- summary(cp) expect_output(print(scp), "Bootstrap summary:") expect_output(print(scp), "Predictor summary:") expect_output(print(scp), "Method: oc_youden_normal") expect_output(print(scp), "Predictor: x") expect_output(print(scp), "Outcome: class") expect_output(print(scp), "Subgroup: female") expect_output(print(scp), "Subgroup: male") expect_output(print(scp), "Direction: >=") expect_output(print(scp), "Nr. of bootstraps: 10") expect_output(print(scp), "accuracy_oob [0-9]*") }) test_that("add_metric adds metrics correctly", { oc <- cutpointr(suicide, dsi, suicide, gender) oc <- add_metric(oc, list(ppv, npv)) expect_equal(oc$ppv, c(0.3676471, 0.2592593), tolerance = 1e-5) expect_equal(oc$npv, c(0.9938272, 0.9823009), tolerance = 1e-5) oc <- cutpointr(suicide, dsi, suicide) oc <- add_metric(oc, list(F1_score, precision)) expect_equal(oc$F1_score, 0.470588, tolerance = 1e-5) expect_equal(oc$precision, 0.32, tolerance = 1e-5) oc <- cutpointr(suicide, dsi, suicide, use_midpoints = T) oc <- add_metric(oc, list(abs_d_ppv_npv, abs_d_sens_spec)) expect_equal(oc$abs_d_ppv_npv, 0.670741, tolerance = 1e-5) expect_equal(oc$abs_d_sens_spec, 0.0259857, tolerance = 1e-5) rcp <- roc(data = suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") %>% add_metric(list(cohens_kappa, F1_score)) expect_equal(rcp$cohens_kappa, c(0.00000000, 0.05058128, 0.09312293, 0.13833841, 0.18161477, 0.30098108, 0.52746652, 0.52329749, 0.47076829, 0.42463778, 0.41208251, 0.27878034, 0.00000000), tolerance = 1e-5) tempdat <- data.frame(y = c(0,0,0,1,0,1,1,1), x = 1:8) oc <- cutpointr(tempdat, x, y, break_ties = c) oc <- add_metric(oc, list(recall, youden)) expect_equal(oc$recall[[1]], c(0.75, 1)) expect_equal(oc$youden[[1]], c(0.75, 0.75)) tempdat_g <- data.frame(g = c(rep(1, 8), rep(2, 8)), y = c(tempdat$y, tempdat$y), x = c(tempdat$x, tempdat$x + 2)) oc <- cutpointr(tempdat_g, x = x, class = y, pos_class = 1, direction = ">=", subgroup = g, break_ties = c) oc <- add_metric(oc, list(false_omission_rate, prod_sens_spec)) expect_equal(oc$false_omission_rate[[1]], c(0.2, 0)) expect_equal(oc$prod_sens_spec[[1]], c(0.75, 0.75)) mymetric <- function(...) return(42) oc <- cutpointr(suicide, dsi, suicide) oc <- add_metric(oc, list(mymetric)) expect_equal(oc$added_metric, 42) mymetric <- function(...) return(data.frame(mymet = 42)) oc <- cutpointr(suicide, dsi, suicide) oc <- add_metric(oc, list(mymetric)) expect_equal(oc$mymet, 42) set.seed(8623) mcp <- multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide, subgroup = gender, pos_class = "yes", boot_runs = 3) mcp <- mcp %>% add_metric(list(ppv)) expect_equal(mcp$ppv, c(0.07386364, 0.50000000, 0.36764706, 0.25925926)) }) test_that("cutpointr works if method / metric are called with ::", { expect_silent(cutpointr(suicide, dsi, suicide, method = cutpointr::maximize_boot_metric, metric = cutpointr::accuracy, silent = TRUE)) expect_silent(cutpointr(suicide$dsi, suicide$suicide, method = cutpointr::maximize_boot_metric, metric = cutpointr::cohens_kappa, silent = TRUE)) }) test_that("Plotting with multi_cutpointr throws error", { expect_error(plot( multi_cutpointr(suicide, x = c("age", "dsi"), class = "suicide", pos_class = "yes") )) expect_error(plot( multi_cutpointr(suicide, x = c("age", "dsi"), subgroup = "gender", class = "suicide", pos_class = "yes") )) }) test_that("Summary(multi_cutpointr) is silent", { expect_silent( smcp <- summary( multi_cutpointr(suicide, x = c("age", "dsi"), class = "suicide", pos_class = "yes", silent = TRUE) ) ) expect_silent( smcp <- summary( multi_cutpointr(suicide, x = c("age", "dsi"), class = "suicide", subgroup = "gender", pos_class = "yes", silent = TRUE) ) ) expect_silent( smcp <- summary( multi_cutpointr(suicide, x = c("age", "dsi"), class = "suicide", boot_runs = 5, pos_class = "yes", silent = TRUE) ) ) expect_silent( smcp <- summary( multi_cutpointr(suicide, x = c("age", "dsi"), class = "suicide", subgroup = "gender", boot_runs = 5, pos_class = "yes", silent = TRUE) ) ) }) test_that("multi_cutpointr fetches numeric columns correctly", { tempdat <- iris[1:99, ] tempdat$char <- "XYZ" set.seed(734) tempdat$g <- sample(0:1, size = 99, replace = TRUE) expect_silent( mcp <- multi_cutpointr(tempdat, class = "Species", silent = TRUE) ) expect_equal(nrow(mcp), 5) expect_silent( mcp <- multi_cutpointr(tempdat, class = "Species", subgroup = "g", silent = TRUE) ) expect_equal(nrow(mcp), 8) expect_silent( mcp <- multi_cutpointr(tempdat, class = "Species", subgroup = "g", silent = TRUE, boot_runs = 10) ) expect_equal(nrow(mcp), 8) }) test_that("tidyeval works with cutpointr", { myvar <- "dsi" myclass <- "suicide" mygroup <- "gender" cp <- cutpointr(suicide, !!myvar, !!myclass, !!mygroup) cp2 <- cutpointr(suicide, dsi, suicide, gender) expect_identical(cp %>% dplyr::select(-data, -roc_curve), cp2 %>% dplyr::select(-data, -roc_curve)) expect_silent(summary(cp)) }) test_that("tidyeval works with roc", { myvar <- "dsi" myclass <- "suicide" r <- roc(suicide, !!myvar, !!myclass, pos_class = "yes", neg_class = "no") expect_identical( roc(suicide, dsi, suicide, pos_class = "yes", neg_class = "no"), r ) expect_silent(auc(r)) }) test_that("boot_ci works correctly", { set.seed(1349) cp <- cutpointr(suicide, dsi, suicide, boot_runs = 30) bci <- boot_ci(x = cp, variable = optimal_cutpoint, alpha = 0.05) expect_equal(round(as.numeric(bci$values), 3), c(1, 3.275)) set.seed(1349) cp <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 30) bci <- boot_ci(x = cp, variable = optimal_cutpoint, alpha = 0.05) expect_equal(as.numeric(round(bci$values, 3)), c(2, 2, 1, 4.55)) expect_equal(bci$subgroup, c("female", "female", "male", "male")) }) test_that("boot_test works correctly", { set.seed(123) cp_f <- cutpointr(suicide %>% dplyr::filter(gender == "female"), dsi, suicide, boot_runs = 10, boot_stratify = T) set.seed(924) cp_m <- cutpointr(suicide %>% dplyr::filter(gender == "male"), dsi, suicide, boot_runs = 10, boot_stratify = T) bt <- boot_test(cp_f, cp_m, AUC, in_bag = TRUE) expect_equal(round(as.numeric(bt$p), 3), 0.175) expect_equal(round(as.numeric(bt$z), 2), 1.36) set.seed(9184) cp <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 10) btg <- boot_test(cp, variable = AUC, in_bag = TRUE) expect_equal(as.numeric(round(btg$p, 3)), 0.139) expect_equal(btg$subgroup1, "female") expect_equal(as.numeric(btg$d), as.numeric(bt$d)) dat <- suicide set.seed(765) dat$g <- sample(c("a", "b", "c"), size = nrow(suicide), replace = TRUE) set.seed(745) cp <- cutpointr(dat, dsi, suicide, g, boot_runs = 15, boot_stratify = TRUE, metric = youden) bt <- boot_test(cp, variable = youden) expect_equal(nrow(bt), 3) expect_equal(as.numeric(round(bt$p, 3)), c(0.731, 0.274, 0.378)) expect_equal(as.numeric(round(bt$p_adj, 3)), c(0.821, 0.821, 0.821)) bt <- boot_test(cp, variable = youden, correction = "bonferroni") expect_equal(as.numeric(round(bt$p_adj, 3)), c(1, 0.821, 1)) }) ## runtime too long # test_that("Bootstrap works with multiple cutpoints when not breaking ties", { # set.seed(827) # tempdat <- data.frame(x = rnorm(1000), # y = sample(0:1, size = 1000, replace = TRUE), # g = sample(0:1, size = 1000, replace = TRUE)) # set.seed(73) # cp <- cutpointr(tempdat, x, y, break_ties = c, boot_runs = 200) # expect_silent(summary(cp)) # # set.seed(73) # cp <- cutpointr(tempdat, x, y, g, break_ties = c, boot_runs = 100) # expect_silent(summary(cp)) # }) test_that("additional metric parameters are passed to bootstrap", { my_metric <- function(tp, fp, tn, fn, myparam, ...) { sens <- sensitivity(tp = tp, fn = fn) spec <- specificity(fp = fp, tn = tn) iu <- sens + spec + myparam iu <- matrix(iu, ncol = 1) colnames(iu) <- "myparam" return(iu) } set.seed(19349) cp <- 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(has_column(x$cutpointr[[i]], "subgroup")) { cat(paste("Subgroup:", x$cutpointr[[i]]$subgroup, "\n")) cat(paste0(rep("-", getOption("width")), collapse = ""), "\n") } tempdat <- x$cutpointr[[i]] %>% dplyr::select(.data$AUC) %>% round(digits = digits) %>% dplyr::mutate(n = x$n_obs[i], n_pos = x$n_pos[i], n_neg = x$n_neg[i]) %>% as.data.frame() print_df_nodat(tempdat, row.names = FALSE) cat("\n") purrr::map_df(1:length(x$cutpointr[[i]]$optimal_cutpoint[[1]]), function(j) { x$cutpointr[[i]] %>% # dplyr::select(-c(direction, method, AUC:boot)) %>% dplyr::select(!tidyselect::any_of(c("direction", "subgroup", "method", "AUC", "pos_class", "neg_class", "prevalence", "outcome", "predictor", "grouping", "data", "roc_curve", "boot"))) %>% purrr::map_df(get_fnth, n = j) }) %>% as.data.frame %>% dplyr::left_join(y = x$confusion_matrix[[i]], by = c("optimal_cutpoint" = "cutpoint")) %>% round(digits = digits) %>% print_df_nodat(row.names = FALSE) cat("\n") cat(paste("Predictor summary:", "\n")) print_df_nodat(rbind(x$desc[[i]], x$desc_by_class[[i]])) if (has_boot_results(x[i, ])) { cat("\n") cat(paste("Bootstrap summary:", "\n")) print_df_nodat( x[["boot"]][[i]] %>% dplyr::mutate_if(is.numeric, round, digits = boot_digits), row.names = rep("", nrow(x[["boot"]][[i]])) ) } } return(invisible(x)) } cutpointr/R/grid_arrange_shared_legend.R0000644000176200001440000000376214726066534020142 0ustar liggesusers#' @source Forked from https://github.com/tidyverse/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs grid_arrange_shared_legend <- function(plots, ncol = length(plots), nrow = 1, position = c("bottom", "right")) { position <- match.arg(position) g <- ggplot2::ggplotGrob(plots[[1]] + ggplot2::theme(legend.position = position))$grobs gb <- sapply(g, function(x) x$name) == "guide-box" # No legend because only one class: if (!all(!gb)) { legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]] lheight <- sum(legend$height) lwidth <- sum(legend$width) } gl <- lapply(plots, function(x) x + ggplot2::theme(legend.position="none")) if (!all(!gb)) gl <- c(gl, ncol = ncol, nrow = nrow) if (!all(!gb)) { if (position == "bottom") { hei <- grid::unit.c(grid::unit(1, "npc") - lheight, lheight) combined <- gridExtra::arrangeGrob(do.call(gridExtra::arrangeGrob, gl), legend, ncol = 1, heights = hei) } else if (position == "right") { wid <- grid::unit.c(grid::unit(1, "npc") - lwidth, lwidth) combined <- gridExtra::arrangeGrob(do.call(gridExtra::arrangeGrob, gl), legend, ncol = 2, widths = wid) } } else { # No legend because only one class. Different methods to plot ROC # beneath dist, not under, if length(gl) == 2 if (length(gl) == 2) { combined <- gridExtra::grid.arrange(gl[[1]], gl[[2]], ncol = 2, nrow = 1) } else { combined <- gridExtra::arrangeGrob(do.call(gridExtra::arrangeGrob, gl), ncol = 1, nrow = 1) } } grid::grid.newpage() grid::grid.draw(combined) invisible(combined) } cutpointr/R/plot.cutpointr.R0000644000176200001440000000321114726066534015643 0ustar liggesusers#' Plot cutpointr objects #' #' The plot layout depends on whether subgroups were defined and whether #' bootstrapping was run. #' #' The \code{...} argument can be used to apply \pkg{ggplot2} functions to every individual #' plot, for example for changing the theme. #' #' @param x A cutpointr object. #' @param ... Further arguments. #' @examples #' opt_cut <- cutpointr(suicide, dsi, suicide, gender) #' plot(opt_cut) #' plot(opt_cut, ggplot2::theme_bw()) #' @family cutpointr plotting functions #' @export plot.cutpointr <- function(x, ...) { if ("multi_cutpointr" %in% class(x)) { stop("Plotting multi_cutpointr objects is not supported.") } args <- list(...) dist <- plot_x(x) roc <- plot_roc(x) boot_flag <- has_boot_results(x) if (boot_flag) { boot_cut <- plot_cut_boot(x) boot_metric <- plot_metric_boot(x) } else { boot_cut <- NULL boot_metric <- NULL } # # Compose plots # plots <- list(dist, roc, boot_cut, boot_metric) keep <- !(purrr::map_lgl(plots, is.null)) plots <- plots[keep] plots <- lapply(plots, function(p) p + args) rows <- round(sum(keep) / 2) pos <- ifelse(rows > 1, "right", "bottom") # suppressMessages(p <- grid_arrange_shared_legend(plots, # nrow = rows, ncol = 2, # position = pos)) # invisible(p) grid_arrange_shared_legend(plots, nrow = rows, ncol = 2, position = pos) } cutpointr/R/RcppExports.R0000644000176200001440000000172214726125562015132 0ustar liggesusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 any_inf <- function(x) { .Call('_cutpointr_any_inf', PACKAGE = 'cutpointr', x) } get_rev_dups <- function(x) { .Call('_cutpointr_get_rev_dups', PACKAGE = 'cutpointr', x) } is_equal_cpp_char <- function(x, y) { .Call('_cutpointr_is_equal_cpp_char', PACKAGE = 'cutpointr', x, y) } is_equal_cpp_num <- function(x, y) { .Call('_cutpointr_is_equal_cpp_num', PACKAGE = 'cutpointr', x, y) } one_unique_num <- function(x) { .Call('_cutpointr_one_unique_num', PACKAGE = 'cutpointr', x) } one_unique_char <- function(x) { .Call('_cutpointr_one_unique_char', PACKAGE = 'cutpointr', x) } which_are_num <- function(x, a) { .Call('_cutpointr_which_are_num', PACKAGE = 'cutpointr', x, a) } which_are_char <- function(x, a) { .Call('_cutpointr_which_are_char', PACKAGE = 'cutpointr', x, a) } cutpointr/R/print.cutpointr.R0000644000176200001440000000165214726066534016030 0ustar liggesusers#' Print cutpointr objects #' #' Prints the \code{cutpointr} object with full width like a \code{tbl_df}. #' #' @source Kirill Müller and Hadley Wickham (2017). tibble: Simple Data Frames. #' https://CRAN.R-project.org/package=tibble #' @param x a cutpointr object. #' @param width width of output. #' @param n number of rows to print. #' @param sigfig Number of significant digits to print. Temporarily #' overrides options("pillar.sigfig"). #' @param ... further arguments. #' @export print.cutpointr <- function(x, width = 1000, n = 50, sigfig = 6, ...) { # print.tbl_df is not exported by tibble, avoid ::: class(x) <- c("tbl_df", "tbl", "data.frame") old_sigfig <- options("pillar.sigfig") options(pillar.sigfig = sigfig) print(x, width = width, n = n) options(pillar.sigfig = old_sigfig$pillar.sigfig) class(x) <- c("cutpointr", "tbl_df", "tbl", "data.frame") invisible(x) } cutpointr/R/print.multi_cutpointr.R0000644000176200001440000000073014726066534017236 0ustar liggesusers#' Print multi_cutpointr objects #' #' Prints the \code{multi_cutpointr} object with infinite width like a \code{tbl_df}. #' #' @source Kirill Müller and Hadley Wickham (2017). tibble: Simple Data Frames. #' https://CRAN.R-project.org/package=tibble #' @param x a multi_cutpointr object. #' @param n number of rows to print. #' @param ... further arguments. #' @export print.multi_cutpointr <- function(x, n = Inf, ...) { print.cutpointr(x, n = n, ...) }cutpointr/R/plot.multi_cutpointr.R0000644000176200001440000000054114726066534017060 0ustar liggesusers#' Plotting multi_cutpointr objects is currently not supported #' #' You can try plotting the data manually instead. #' #' @param x A multi_cutpointr object. #' @param ... Further arguments. #' #' @export plot.multi_cutpointr <- function(x, ...) { stop("Plotting multi_cutpointr objects is not supported.") return(invisible(NULL)) } cutpointr/R/oc_youden_normal.R0000644000176200001440000000550114726066534016177 0ustar liggesusers#' Determine an optimal cutpoint for the Youden-Index assuming normal distributions #' #' An optimal cutpoint maximizing the Youden- or J-Index #' (sensitivity + specificity - 1) is calculated parametrically assuming #' normal distributions per class. #' #' @param data A data frame or tibble in which the columns that are given in x #' and class can be found. #' @param x (character) The variable name to be used for classification, #' e.g. predictions or test values. #' @param class (character) The variable name indicating class membership. #' @param pos_class The value of class that indicates the positive class. #' @param neg_class The value of class that indicates the negative class. #' @param direction (character) Use ">=" or "<=" to select whether an x value #' >= or <= the cutoff predicts the positive class. #' @param ... To capture further arguments that are always passed to the method #' function by cutpointr. The cutpointr function passes data, x, class, #' metric_func, direction, pos_class and neg_class to the method function. #' @examples #' data(suicide) #' oc_youden_normal(suicide, "dsi", "suicide", #' pos_class = "yes", neg_class = "no", direction = ">=") #' cutpointr(suicide, dsi, suicide, method = oc_youden_normal) #' @family method functions #' @export oc_youden_normal <- function(data, x, class, pos_class = NULL, neg_class = NULL, direction, ...) { stopifnot(is.character(x)) stopifnot(is.character(class)) iv <- unlist(data[, x]) if (any_inf(iv)) stop("Only finite values allowed in oc_youden_normal") cla <- unlist(data[, class]) if (direction %in% c(">", ">=")) { patients <- iv[cla == pos_class] controls <- iv[cla == neg_class] } else if (direction %in% c("<", "<=")) { patients <- iv[cla == neg_class] controls <- iv[cla == pos_class] } m_h <- mean(controls) sd_h <- stats::sd(controls) m_d <- mean(patients) sd_d <- stats::sd(patients) if (sd_h == sd_d) { c <- (m_h+m_d)/2 } else if (any(sd_h == 0, sd_d == 0)) { # if sd_h = 0 and/or sd_d = 0 the cutoff would be NaN c <- (m_h+m_d)/2 } else { c <- ((m_d*sd_h^2 - m_h*sd_d^2) - sd_h*sd_d*(sqrt((m_h-m_d)^2 + (sd_h^2-sd_d^2) * log(sd_h^2/sd_d^2)))) / (sd_h^2-sd_d^2) } # Extremely high or low cutoffs can result if m_d < m_h and direction = ">=" if (c < min(c(controls, patients))) { warning(paste("Cutpoint", c, "was restricted to range of independent variable")) c <- min(c(controls, patients)) } else if (c > max(c(controls, patients))) { warning(paste("Cutpoint", c, "was restricted to range of independent variable")) c <- max(c(controls, patients)) } return(data.frame(optimal_cutpoint = c)) } cutpointr/R/predict.cutpointr.R0000644000176200001440000000653214726066534016330 0ustar liggesusers#' Predict using a cutpointr object #' #' Predictions are made on the \code{data.frame} in \code{newdata} #' using either the variable name or by applying the same transformation to #' the data as in \code{cutpointr}. The class of the output will be identical to the class #' of the predictor. #' #' @param object a cutpointr object. #' @param newdata a data.frame with a column that contains the predictor #' variable. #' @param cutpoint_nr if multiple optimal cutpoints were found this parameter #' defines which one should be used for predictions. Can be a vector if #' different cutpoint numbers are desired for different subgroups. #' @param ... further arguments. #' @examples #' oc <- cutpointr(suicide, dsi, suicide) #' ## Return in-sample predictions #' predict(oc, newdata = data.frame(dsi = oc$data[[1]]$dsi)) #' @family main cutpointr functions #' @export predict.cutpointr <- function(object, newdata, cutpoint_nr = 1, ...) { if (!("data.frame" %in% class(newdata))) { stop("newdata should be a data.frame") } if (!(length(cutpoint_nr) == 1 | length(cutpoint_nr) == nrow(object))) { stop("Specify one cutpoint_nr or one cutpoint_nr per subgroup.") } predictor_name <- object$predictor[1] # The predictor may have been altered using NSE indep_var <- eval(parse(text = predictor_name), newdata, parent.frame()) pos_class <- object$pos_class[1] neg_class <- object$neg_class[1] if (any(colnames(object) == "subgroup")) { grouping_name <- unique(object$grouping) grouping_var_new <- eval(parse(text = grouping_name), newdata, parent.frame()) opt_cut_ind <- purrr::map_int(grouping_var_new, function(g) { which(object$subgroup == g) }) if (length(cutpoint_nr) == 1) { optimal_cuts <- purrr::map_dbl(object$optimal_cutpoint, function(x) { x[cutpoint_nr] }) } else { optimal_cuts <- purrr::map2(.x = 1:nrow(object), .y = cutpoint_nr, .f = function(i, nr) { optimal_cut <- object$optimal_cutpoint[[i]][nr] if (is.na(optimal_cut)) { stop(paste("Cutpoint Nr.", nr, "in subgroup", i, "not found")) } return(optimal_cut) }) optimal_cuts <- unlist(optimal_cuts) } optimal_cuts <- optimal_cuts[opt_cut_ind] if (object$direction[1] == ">=") { preds <- indep_var >= optimal_cuts preds <- ifel_pos_neg(preds, pos_class, neg_class) } else if (object$direction[1] == "<=") { preds <- indep_var <= optimal_cuts preds <- ifel_pos_neg(preds, pos_class, neg_class) } } else { optimal_cut <- object$optimal_cutpoint[[1]][cutpoint_nr] if (is.na(optimal_cut)) { stop(paste("Cutpoint Nr.", cutpoint_nr, "not found")) } if (object$direction[1] == ">=") { preds <- indep_var >= optimal_cut preds <- ifel_pos_neg(preds, pos_class, neg_class) } else if (object$direction[1] == "<=") { preds <- indep_var <= optimal_cut preds <- ifel_pos_neg(preds, pos_class, neg_class) } } return(preds) }cutpointr/R/prep.R0000644000176200001440000000430314726066534013610 0ustar liggesusersassume_direction_pos_class <- function(x, class, pos_class, neg_class, direction, na.rm, uc) { # Handle NAs if (na.rm) { na_indx <- is.na(x) na_indc <- is.na(class) complete_ind <- !(na_indx + na_indc) x <- x[complete_ind] class <- class[complete_ind] } if (is.null(direction) & !is.null(pos_class)) { if (stats::median(x[class != pos_class]) < stats::median(x[class == pos_class])) { message("Assuming the positive class has higher x values") direction <- ">=" } else { message("Assuming the positive class has lower x values") direction <- "<=" } } if (is.null(direction) & is.null(pos_class)) direction <- ">=" if (!is.null(direction) & is.null(pos_class)) { if (direction == ">" | direction == ">=") { if (stats::median(x[class == uc[1]]) > stats::median(x[class == uc[2]])) { message(paste("Assuming the positive class is", uc[1])) message("Assuming the positive class has higher x values") pos_class <- uc[1] } else { message(paste("Assuming the positive class is", uc[2])) message("Assuming the positive class has higher x values") pos_class <- uc[2] } } else { if (stats::median(x[class == uc[1]]) < stats::median(x[class == uc[2]])) { message(paste("Assuming the positive class is", uc[1])) message("Assuming the positive class has lower x values") pos_class <- uc[1] } else { message(paste("Assuming the positive class is", uc[2])) message("Assuming the positive class has lower x values") pos_class <- uc[2] } } } if (!any(pos_class == class)) stop("Positive class not found in data") if (is.null(neg_class)) { neg_class <- unique(class) neg_class <- neg_class[neg_class != pos_class] } return(list(direction = direction, pos_class = pos_class, neg_class = neg_class)) } cutpointr/R/plot_metric.R0000644000176200001440000001541414726066534015170 0ustar liggesusers#' Plot a metric over all possible cutoffs from a cutpointr object #' #' If \code{maximize_metric} is used as \code{method} function in cutpointr the computed #' metric values over all possible cutoffs can be plotted. Generally, this #' works for method functions that return a ROC-curve including the metric #' value for every cutpoint along with the optimal cutpoint. #' #' @param x A cutpointr object. #' @param conf_lvl The confidence level of the bootstrap confidence interval. #' Set to 0 to draw no bootstrap confidence interval. #' @param add_unsmoothed Add the line of unsmoothed metric values to the plot. #' Applicable for some smoothing methods, e.g. maximize_gam_metric. #' @examples #' opt_cut <- cutpointr(suicide, dsi, suicide) #' plot_metric(opt_cut) #' @importFrom dplyr %>% #' @family cutpointr plotting functions #' @family cutpointr plotting functions #' @export plot_metric <- function(x, conf_lvl = 0.95, add_unsmoothed = TRUE) { if (!("cutpointr" %in% class(x))) { stop("Only cutpointr objects are supported.") } if (!(has_column(x$roc_curve[[1]], "m"))) { stop(paste("The cutpointr object does not include a metric column in", "roc_curve - maybe because a method other than", "maximize_metric or minimize_metric was used")) } if (has_boot_results(x) & conf_lvl != 0) { if (has_column(x, "subgroup")) { roc_b_unnested <- x %>% dplyr::select(c("boot", "subgroup")) %>% dplyr::mutate(boot = prepare_bind_rows(.data$boot)) %>% tidyr::unnest(.data$boot) %>% dplyr::select(c("subgroup", "roc_curve_b")) %>% tidyr::unnest(.data$roc_curve_b) roc_b_unnested <- roc_b_unnested[is.finite(roc_b_unnested$x.sorted), ] roc_b_unnested <- roc_b_unnested %>% dplyr::select(c("x.sorted", "m", "subgroup")) %>% dplyr::group_by(.data$x.sorted, .data$subgroup) %>% dplyr::summarise(ymin = stats::quantile(.data$m, (1 - conf_lvl) / 2, na.rm = TRUE), ymax = stats::quantile(.data$m, 1 - (1 - conf_lvl) / 2, na.rm = TRUE)) } else { # No subgroups, but bootstrap roc_b_unnested <- x[["boot"]][[1]] %>% tidyr::unnest(.data$roc_curve_b) roc_b_unnested <- roc_b_unnested[is.finite(roc_b_unnested$x.sorted), ] roc_b_unnested <- roc_b_unnested %>% dplyr::select(c("x.sorted", "m")) %>% dplyr::group_by(.data$x.sorted) %>% dplyr::summarise(ymin = stats::quantile(.data$m, (1 - conf_lvl) / 2, na.rm = TRUE), ymax = stats::quantile(.data$m, 1 - (1 - conf_lvl) / 2, na.rm = TRUE)) } } metric_name <- find_metric_name(x) if ("subgroup" %in% colnames(x)) { res_unnested <- x %>% dplyr::select(c("roc_curve", "subgroup")) %>% tidyr::unnest(.data$roc_curve) res_unnested <- res_unnested[is.finite(res_unnested$x.sorted), ] if (has_boot_results(x) & conf_lvl != 0) { res_unnested <- merge(res_unnested, roc_b_unnested[, c("subgroup", "x.sorted", "ymin", "ymax")], by = c("x.sorted", "subgroup")) p <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = x.sorted, y = m, ymin = ymin, ymax = ymax, color = subgroup, fill = subgroup)) + ggplot2::geom_line() + ggplot2::geom_point() + ggplot2::ylab(metric_name) + ggplot2::xlab("Cutpoint") + ggplot2::geom_ribbon(alpha = 0.2, size = 0) } else { p <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = x.sorted, y = m, color = subgroup)) + ggplot2::geom_line() + ggplot2::geom_point() + ggplot2::ylab(metric_name) + ggplot2::xlab("Cutpoint") } if (add_unsmoothed & has_column(res_unnested, "m_unsmoothed")) { p <- p + ggplot2::geom_line(data = res_unnested, linetype = "dashed", mapping = ggplot2::aes(x = x.sorted, y = m_unsmoothed, color = subgroup)) } } else { # No subgroups res_unnested <- x %>% dplyr::select(.data$roc_curve) %>% tidyr::unnest(.data$roc_curve) res_unnested <- res_unnested[is.finite(res_unnested$x.sorted), ] if (has_boot_results(x) & conf_lvl != 0) { res_unnested <- merge(res_unnested, roc_b_unnested[, c("x.sorted", "ymin", "ymax")], by = "x.sorted") p <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = x.sorted, y = m, ymax = ymax, ymin = ymin)) + ggplot2::geom_line() + ggplot2::geom_point() + ggplot2::ylab(metric_name) + ggplot2::xlab("Cutpoint") + ggplot2::geom_ribbon(alpha = 0.2, size = 0) } else { p <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = x.sorted, y = m)) + ggplot2::geom_line() + ggplot2::geom_point() + ggplot2::ylab(metric_name) + ggplot2::xlab("Cutpoint") } if (add_unsmoothed & has_column(res_unnested, "m_unsmoothed")) { p <- p + ggplot2::geom_line(data = res_unnested, linetype = "dashed", mapping = ggplot2::aes(x = x.sorted, y = m_unsmoothed)) } } if (add_unsmoothed & has_column(res_unnested, "m_unsmoothed")) { p <- p + ggplot2::ggtitle("Metric values by cutpoint value", "in-sample results, unsmoothed values as dashed line") } else { p <- p + ggplot2::ggtitle("Metric values by cutpoint value", "in-sample results") } return(p) } cutpointr/R/plot_roc.R0000644000176200001440000000713514726066534014471 0ustar liggesusers#' Plot ROC curve from a cutpointr or roc_cutpointr object #' #' Given a \code{cutpointr} object this function plots the ROC curve(s) #' per subgroup, if given. Also plots a ROC curve from the output of \code{roc()}. #' @param x A cutpointr or roc_cutpointr object. #' @param display_cutpoint (logical) Whether or not to display the optimal #' cutpoint as a dot on the ROC curve for cutpointr objects. #' @param type "line" for line plot (default) or "step" for step plot. #' @param ... Additional arguments (unused). #' @examples #' opt_cut <- cutpointr(suicide, dsi, suicide) #' plot_roc(opt_cut, display_cutpoint = FALSE) #' #' opt_cut_2groups <- cutpointr(suicide, dsi, suicide, gender) #' plot_roc(opt_cut_2groups, display_cutpoint = TRUE) #' #' roc_curve <- roc(suicide, x = dsi, class = suicide, pos_class = "yes", #' neg_class = "no", direction = ">=") #' plot(roc_curve) #' auc(roc_curve) #' @family cutpointr plotting functions #' @name plot_roc #' @export plot_roc <- function(x, ...) { UseMethod("plot_roc", x) } #' @rdname plot_roc #' @export plot_roc.cutpointr <- function(x, display_cutpoint = TRUE, type = "line", ...) { predictor <- as.name(x$predictor[1]) outcome <- as.name(x$outcome[1]) if (!(has_column(x, "subgroup"))) { dts_roc <- "roc_curve" transparency <- 1 } else { dts_roc <- c("roc_curve", "subgroup") transparency <- 0.6 } roc_title <- ggplot2::ggtitle("ROC curve") if (display_cutpoint) { optcut_coords <- purrr::pmap_df(x, function(...) { args <- list(...) opt_ind <- get_opt_ind(roc_curve = args$roc_curve, oc = args$optimal_cutpoint, direction = args$direction) data.frame(tpr = args$roc_curve$tpr[opt_ind], tnr = args$roc_curve$tnr[opt_ind]) }) } res_unnested <- x %>% dplyr::select(dts_roc) %>% tidyr::unnest(.data$roc_curve) if (!(has_column(x, "subgroup"))) { roc <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = 1 - tnr, y = tpr)) + roc_title + ggplot2::xlab("1 - Specificity") + ggplot2::ylab("Sensitivity") + ggplot2::theme(aspect.ratio = 1) } else if (has_column(x, "subgroup")) { roc <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = 1 - tnr, y = tpr, color = subgroup)) + roc_title + ggplot2::xlab("1 - Specificity") + ggplot2::ylab("Sensitivity") + ggplot2::theme(aspect.ratio = 1) } if (display_cutpoint) { roc <- roc + ggplot2::geom_point(data = optcut_coords, color = "black") } if (type == "line") { roc <- roc + ggplot2::geom_line() } else if (type == "step") { roc <- roc + ggplot2::geom_step() } return(roc) } #' @rdname plot_roc #' @export plot_roc.roc_cutpointr <- function(x, type = "line", ...) { roc_title <- ggplot2::ggtitle("ROC curve") roc <- ggplot2::ggplot(x, ggplot2::aes(x = 1 - tnr, y = tpr)) + roc_title + ggplot2::xlab("1 - Specificity") + ggplot2::ylab("Sensitivity") + ggplot2::theme(aspect.ratio = 1) if (type == "line") { roc <- roc + ggplot2::geom_line() } else if (type == "step") { roc <- roc + ggplot2::geom_step() } return(roc) } #' @inherit plot_roc #' @export plot.roc_cutpointr <- function(x, type = "line", ...) { plot_roc(x, type = type) }cutpointr/R/plot_cutpointr.R0000644000176200001440000002305514726066534015734 0ustar liggesusers#' General purpose plotting function for cutpointr or roc_cutpointr objects #' #' Flexibly plot various metrics against all cutpoints or any other metric. #' The function can plot any metric based on a \code{cutpointr} or \code{roc_cutpointr} #' object. If \code{cutpointr} was run with bootstrapping, bootstrapped confidence #' intervals can be plotted. These represent the quantiles of the distribution #' of the y-variable grouped by x-variable over all bootstrap repetitions. #' #' The arguments to \code{xvar} and \code{yvar} should be metric functions. Any metric #' function that is suitable for \code{cutpointr} can also be used in \code{plot_cutpointr}. #' Anonymous functions are also allowed. #' To plot all possible cutpoints, the utility function \code{cutpoint} can be used. #' #' The functions for \code{xvar} and \code{yvar} may accept any or all of the arguments #' \code{tp}, \code{fp}, \code{tn}, or \code{fn} and return a numeric vector, #' a matrix or a \code{data.frame}. #' For more details on metric functions see \code{vignette("cutpointr")}. #' #' Note that confidence intervals can only be correctly plotted if the values of \code{xvar} #' are constant across bootstrap samples. For example, confidence intervals for #' \code{tpr} by \code{fpr} (a ROC curve) cannot be plotted, as the values of the false positive #' rate vary per bootstrap sample. #' #' @param x A \code{cutpointr} or \code{roc_cutpointr} object. #' @param xvar A function, typically \code{cutpoint} or a metric function. #' @param yvar A function, typically a metric function. #' @param conf_lvl (numeric) If bootstrapping was run and x is a cutpointr object, #' a confidence interval at the level of conf_lvl can be plotted. To plot no #' confidence interval set conf_lvl = 0. #' @param aspect_ratio (numeric) Set to 1 to obtain a quadratic plot, e.g. for #' plotting a ROC curve. #' #' @examples #' set.seed(1) #' oc <- cutpointr(suicide, dsi, suicide, boot_runs = 10) #' #' plot_cutpointr(oc, cutpoint, F1_score) #' #' ## ROC curve #' plot_cutpointr(oc, fpr, tpr, aspect_ratio = 1) #' #' ## Custom function #' plot_cutpointr(oc, cutpoint, function(tp, tn, fp, fn, ...) tp / fp) + #' ggplot2::ggtitle("Custom metric") + ggplot2::ylab("value") #' #' @family cutpointr plotting functions #' @export plot_cutpointr <- function(x, xvar = cutpoint, yvar = sum_sens_spec, conf_lvl = 0.95, aspect_ratio = NULL) { if (!("cutpointr" %in% class(x) | "roc_cutpointr" %in% class(x))) { stop("plot_cutpointr only supports cutpointr and roc_cutpointr objects.") } if ("cutpointr" %in% class(x)) { rocdat <- x$roc_curve subgroup <- suppressWarnings(x$subgroup) } else { rocdat <- list(x) subgroup <- NULL } xvar_name <- paste(as.character(substitute(xvar)), collapse = " ") yvar_name <- paste(as.character(substitute(yvar)), collapse = " ") xvar_name_plotlabel <- xvar_name yvar_name_plotlabel <- yvar_name rocdat <- purrr::map(.x = rocdat, .f = function(x) { met <- xvar(x = x, tp = x$tp, fp = x$fp, tn = x$tn, fn = x$fn) met_name <- colnames(met) if (is.null(met_name)) { met_name <- xvar_name } else { xvar_name <<- met_name xvar_name_plotlabel <<- met_name } met <- sanitize_metric(met, m_name = met_name, n = nrow(x)) met <- check_metric_name(met) xvar_name <<- colnames(met) met <- tibble::as_tibble(met) class(met) <- class(x) # Avoid warning from vctrs x <- dplyr::bind_cols(x, met) x }) rocdat <- purrr::map(.x = rocdat, .f = function(x) { met <- yvar(x = x, tp = x$tp, fp = x$fp, tn = x$tn, fn = x$fn) met_name <- colnames(met) if (is.null(met_name)) { met_name <- xvar_name } else { yvar_name <<- met_name yvar_name_plotlabel <<- met_name } met <- sanitize_metric(met, m_name = yvar_name, n = nrow(x)) met <- check_metric_name(met) yvar_name <<- colnames(met) met <- tibble::as_tibble(met) class(met) <- class(x) # Avoid warning from vctrs x <- dplyr::bind_cols(x, met) x }) if (has_boot_results(x) & conf_lvl != 0) { # Add xvar and yvar columns to ROC curves of bootstrap repetitions for (i in 1:nrow(x)) { x[["boot"]][[i]]$roc_curve_b <- purrr::map(x[["boot"]][[i]]$roc_curve_b, function(x) { met <- xvar(x = x, tp = x$tp, fp = x$fp, tn = x$tn, fn = x$fn) met <- sanitize_metric(met, m_name = xvar_name, n = nrow(x), silent = TRUE) met <- check_metric_name(met) met <- tibble::as_tibble(met) class(met) <- class(x) # Avoid warning from vctrs x <- dplyr::bind_cols(x, met) met <- yvar(x = x, tp = x$tp, fp = x$fp, tn = x$tn, fn = x$fn) met <- sanitize_metric(met, m_name = yvar_name, n = nrow(x), silent = TRUE) met <- check_metric_name(met) met <- tibble::as_tibble(met) class(met) <- class(x) # Avoid warning from vctrs x <- dplyr::bind_cols(x, met) x }) ci <- x[["boot"]][[i]]$roc_curve_b %>% dplyr::bind_rows() %>% dplyr::select(!!rlang::sym(xvar_name), !!rlang::sym(yvar_name)) %>% dplyr::group_by(!!rlang::sym(xvar_name)) %>% dplyr::summarise(ymin = stats::quantile(!!rlang::sym(yvar_name), (1 - conf_lvl) / 2, na.rm = TRUE), ymax = stats::quantile(!!rlang::sym(yvar_name), 1 - (1 - conf_lvl) / 2, na.rm = TRUE)) rocdat[[i]] <- dplyr::left_join(rocdat[[i]], ci, by = xvar_name) if (any(!(rocdat[[i]][[xvar_name]] %in% ci[[xvar_name]]))) warning(paste(x$subgroup[i], "Not all x-values in ROC curve could be joined with bootstrap,", "the bootstrap confidence intervals are possibly misleading,", "see ?plot_cutpointr")) } } if ("subgroup" %in% colnames(x)) { rocdat <- purrr::map2(subgroup, rocdat, function(s, d) { d <- d[is.finite(d[[yvar_name]]), ] d <- d[is.finite(d[[xvar_name]]), ] d$subgroup <- s return(d) }) %>% dplyr::bind_rows() if (has_boot_results(x) & conf_lvl != 0) { p <- ggplot2::ggplot(rocdat, ggplot2::aes_string(x = xvar_name, y = yvar_name, ymax = "ymax", ymin = "ymin", fill = "subgroup", color = "subgroup")) + ggplot2::geom_line() + ggplot2::geom_ribbon(alpha = 0.2, size = 0) + ggplot2::ggtitle(paste(yvar_name_plotlabel, "by", xvar_name_plotlabel), "in-sample results") + ggplot2::ylab(yvar_name_plotlabel) + ggplot2::xlab(xvar_name_plotlabel) } else { p <- ggplot2::ggplot(rocdat, ggplot2::aes_string(x = xvar_name, y = yvar_name, color = "subgroup")) + ggplot2::geom_line() + ggplot2::ggtitle(paste(yvar_name_plotlabel, "by", xvar_name_plotlabel), "in-sample results") + ggplot2::ylab(yvar_name_plotlabel) + ggplot2::xlab(xvar_name_plotlabel) } } else { rocdat <- rocdat[[1]] rocdat <- rocdat[is.finite(rocdat[[yvar_name]]), ] rocdat <- rocdat[is.finite(rocdat[[xvar_name]]), ] if (has_boot_results(x) & conf_lvl != 0) { p <- ggplot2::ggplot(rocdat, ggplot2::aes_string(x = xvar_name, y = yvar_name, ymax = "ymax", ymin = "ymin")) + ggplot2::geom_line() + ggplot2::geom_ribbon(alpha = 0.2, size = 0) + ggplot2::ggtitle(paste(yvar_name_plotlabel, "by", xvar_name_plotlabel), "in-sample results") + ggplot2::ylab(yvar_name_plotlabel) + ggplot2::xlab(xvar_name_plotlabel) } else { p <- ggplot2::ggplot(rocdat, ggplot2::aes_string(x = xvar_name, y = yvar_name)) + ggplot2::geom_line() + ggplot2::ggtitle(paste(yvar_name_plotlabel, "by", xvar_name_plotlabel), "in-sample results") + ggplot2::ylab(yvar_name_plotlabel) + ggplot2::xlab(xvar_name_plotlabel) } } if (!(is.null(aspect_ratio))) p <- p + ggplot2::theme(aspect.ratio = aspect_ratio) return(p) } cutpointr/R/roc.R0000644000176200001440000001066714726066534013437 0ustar liggesusers#' Calculate a ROC curve #' #' Given a \code{data.frame} with a numeric predictor variable and a binary outcome #' variable this function returns a \code{data.frame} that includes all elements of #' the confusion matrix (true positives, false positives, true negatives, #' and false negatives) for every unique value of the predictor variable. #' Additionally, the true positive rate (tpr), false positive rate (fpr), #' true negative rate (tnr) and false negative rate (fnr) are returned. #' #' To enable classifying all observations as belonging to only one class the #' predictor values will be augmented by Inf or -Inf. The returned object can #' be plotted with plot_roc. #' #' This function uses tidyeval to support unquoted arguments. For programming #' with \code{roc} the operator \code{!!} can be used to unquote an argument, #' see the examples. #' #' @param data A data.frame or matrix. Will be converted to a data.frame. #' @param x The name of the numeric predictor variable. #' @param class The name of the binary outcome variable. #' @param pos_class The value of 'class' that represents the positive cases. #' @param neg_class The value of 'class' that represents the negative cases. #' @param direction (character) One of ">=" or "<=". Specifies if the positive #' class is associated with higher values of x (default). #' @param silent If FALSE and the ROC curve contains no positives or negatives, #' a warning is generated. #' @return A data frame with the columns x.sorted, tp, fp, tn, fn, tpr, tnr, fpr, #' and fnr. #' @examples #' roc_curve <- roc(data = suicide, x = dsi, class = suicide, #' pos_class = "yes", neg_class = "no", direction = ">=") #' roc_curve #' plot_roc(roc_curve) #' auc(roc_curve) #' #' ## Unquoting an argument #' myvar <- "dsi" #' roc(suicide, x = !!myvar, suicide, pos_class = "yes", neg_class = "no") #' @export #' @family main cutpointr functions #' @source #' Forked from the \pkg{ROCR} package roc <- function(data, x, class, pos_class, neg_class, direction = ">=", silent = FALSE) { stopifnot(direction %in% c(">=", "<=")) data <- as.data.frame(data) x_sym <- rlang::ensym(x) x_expr <- rlang::enexpr(x_sym) x <- rlang::eval_tidy(expr = x_expr, data = data) class_sym <- rlang::ensym(class) class_expr <- rlang::enexpr(class_sym) class <- rlang::eval_tidy(expr = class_expr, data = data) if (direction == ">=") { pred.order <- order(x, decreasing = TRUE) x.sorted <- x[pred.order] dups <- rev(duplicated(rev(x.sorted))) x.sorted <- x.sorted[!dups] class.sorted <- class[pred.order] tp <- cumsum(is_equal_cpp(class.sorted, pos_class)) tp <- tp[!dups] fp <- cumsum(is_equal_cpp(class.sorted, neg_class)) fp <- fp[!dups] n_pos <- tp[length(tp)] n_neg <- length(class) - n_pos tn <- n_neg - fp fn <- n_pos + n_neg - tp - fp - tn if (!(any_inf(x.sorted))) { x.sorted <- c(Inf, x.sorted) class.sorted <- c(NA, class.sorted) tp <- c(0, tp) fp <- c(0, fp) tn <- c(n_neg, tn) fn <- c(n_pos, fn) } } else if (direction == "<=") { pred.order <- order(x, decreasing = FALSE) x.sorted <- x[pred.order] dups <- rev(duplicated(rev(x.sorted))) x.sorted <- x.sorted[!dups] class.sorted <- class[pred.order] tp <- cumsum(is_equal_cpp(class.sorted, pos_class)) tp <- tp[!dups] fp <- cumsum(is_equal_cpp(class.sorted, neg_class)) fp <- fp[!dups] n_pos <- tp[length(tp)] n_neg <- length(class) - n_pos tn <- n_neg - fp fn <- n_pos + n_neg - tp - fp - tn if (!(any_inf(x.sorted))) { x.sorted <- c(-Inf, x.sorted) class.sorted <- c(NA, class.sorted) tp <- c(0, tp) fp <- c(0, fp) tn <- c(n_neg, tn) fn <- c(n_pos, fn) } } tpr <- tp / n_pos tnr <- tn / n_neg fpr <- 1 - tnr fnr <- 1 - tpr res <- tibble::tibble(x.sorted, tp, fp, tn, fn, tpr, tnr, fpr, fnr) class(res) <- c("roc_cutpointr", class(res)) if (!silent) { if (is.nan(res$tpr[1])) warning("ROC curve contains no positives") if (res$fpr[1] == 0 & res$fpr[nrow(res)] == 0) warning("ROC curve contains no negatives") } return(res) } cutpointr/R/plot_precision_recall.R0000644000176200001440000000501114726066534017212 0ustar liggesusers#' Precision recall plot from a cutpointr object #' #' Given a \code{cutpointr} object this function plots the precision recall curve(s) #' per subgroup, if given. #' @param x A cutpointr object. #' @param display_cutpoint (logical) Whether or not to display the optimal #' cutpoint as a dot on the precision recall curve. #' @param ... Additional arguments (unused). #' @examples #' library(cutpointr) #' #' ## Optimal cutpoint for dsi #' data(suicide) #' opt_cut <- cutpointr(suicide, dsi, suicide) #' plot_precision_recall(opt_cut) #' @family cutpointr plotting functions #' @export plot_precision_recall <- function(x, display_cutpoint = TRUE, ...) { if (!("cutpointr" %in% class(x))) { stop("Only cutpointr objects are supported.") } args <- list(...) if (!(has_column(x, "subgroup"))) { dts_pr <- "roc_curve" transparency <- 1 } else { dts_pr <- c("roc_curve", "subgroup") transparency <- 0.6 } plot_title <- ggplot2::ggtitle("Precision Recall Plot") for (r in 1:nrow(x)) { x$roc_curve[[r]] <- x$roc_curve[[r]] %>% dplyr::mutate(Precision = tp / (tp + fp), Recall = tp / (tp + fn)) } if (display_cutpoint) { optcut_coords <- purrr::pmap_df(x, function(...) { args <- list(...) opt_ind <- get_opt_ind(roc_curve = args$roc_curve, oc = args$optimal_cutpoint, direction = args$direction) data.frame(Precision = args$roc_curve$Precision[opt_ind], Recall = args$roc_curve$Recall[opt_ind]) }) } res_unnested <- x %>% dplyr::select(dts_pr) %>% tidyr::unnest(.data$roc_curve) res_unnested <- res_unnested[is.finite(res_unnested$x.sorted), ] if (!(has_column(x, "subgroup"))) { pr <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = Recall, y = Precision)) } else { pr <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = Recall, y = Precision, color = subgroup)) } pr <- pr + ggplot2::geom_line() + plot_title + ggplot2::theme(aspect.ratio = 1) + ggplot2::coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) if (display_cutpoint) { pr <- pr + ggplot2::geom_point(data = optcut_coords, color = "black") } return(pr) } cutpointr/R/oc_mean.R0000644000176200001440000000206714726066534014250 0ustar liggesusers#' Use the sample mean as cutpoint #' #' The sample mean is calculated and returned as the optimal cutpoint. #' #' @param data A data frame or tibble in which the columns that are given in x #' and class can be found. #' @param x (character) The variable name to be used for classification, #' e.g. predictions or test values. #' @param trim The fraction (0 to 0.5) of observations to be trimmed from each #' end of x before the mean is computed. Values of trim outside that range are #' taken as the nearest endpoint. #' @param ... To capture further arguments that are always passed to the method #' function by cutpointr. The cutpointr function passes data, x, class, #' metric_func, direction, pos_class and neg_class to the method function. #' @examples #' data(suicide) #' oc_mean(suicide, "dsi") #' cutpointr(suicide, dsi, suicide, method = oc_mean) #' @family method functions #' @export oc_mean <- function(data, x, trim = 0, ...) { stopifnot(is.character(x)) return(data.frame(optimal_cutpoint = mean(unlist(data[, x]), trim = trim))) } cutpointr/R/oc_youden_kernel.R0000644000176200001440000000715014726066534016171 0ustar liggesusers#' Determine an optimal cutpoint maximizing the Youden-Index based on kernel smoothed densities #' #' Instead of searching for an optimal cutpoint to maximize (sensitivity + #' specificity - 1) on the ROC curve, this function first smoothes the empirical #' distributions of \code{x} per class. The smoothing is done using a binned kernel #' density estimate. The bandwidth is automatically selected using the direct #' plug-in method. #' #' The functions for calculating the kernel density estimate and the bandwidth #' are both from \pkg{KernSmooth} with default parameters, except for #' the bandwidth selection, which uses the standard deviation as scale estimate. #' #' The cutpoint is estimated as the cutpoint that maximizes the Youden-Index #' given by \eqn{J = max_c {F_N(c) - G_N(c) }} where #' \eqn{J} and \eqn{G} are the smoothed distribution functions. #' #' @inheritParams oc_youden_normal #' @source Fluss, R., Faraggi, D., & Reiser, B. (2005). Estimation of the #' Youden Index and its associated cutoff point. Biometrical Journal, 47(4), 458–472. #' @source Matt Wand (2015). KernSmooth: Functions for Kernel Smoothing #' Supporting Wand & Jones (1995). R package version 2.23-15. #' https://CRAN.R-project.org/package=KernSmooth #' @examples #' data(suicide) #' if (require(KernSmooth)) { #' oc_youden_kernel(suicide, "dsi", "suicide", oc_metric = "Youden", #' pos_class = "yes", neg_class = "no", direction = ">=") #' ## Within cutpointr #' cutpointr(suicide, dsi, suicide, method = oc_youden_kernel) #' } #' @family method functions #' @export oc_youden_kernel <- function(data, x, class, pos_class, neg_class, direction, ...) { stopifnot(is.character(x)) stopifnot(is.character(class)) iv <- unlist(data[, x]) cla <- unlist(data[, class]) if (direction %in% c(">", ">=")) { x_pos <- iv[cla == pos_class] x_neg <- iv[cla == neg_class] } else { x_neg <- iv[cla == pos_class] x_pos <- iv[cla == neg_class] } bw_n <- KernSmooth::dpik(x_neg, "stdev") neg_k <- KernSmooth::bkde(x_neg, bandwidth = bw_n) bw_p <- KernSmooth::dpik(x_pos, "stdev") pos_k <- KernSmooth::bkde(x_pos, bandwidth = bw_p) oc <- suppressWarnings(stats::optimize( f = youden_kern, interval = c(min(c(x_pos, x_neg)), max(c(x_pos, x_neg))), maximum = TRUE, neg_k = neg_k, pos_k = pos_k )$maximum) # Extremely high or low cutoffs can result in some scenarios if (oc < min(c(x_neg, x_pos))) { warning(paste("Cutpoint", oc, "was restricted to range of independent variable")) oc <- min(c(x_neg, x_pos)) } else if (oc > max(c(x_neg, x_pos))) { warning(paste("Cutpoint", oc, "was restricted to range of independent variable")) oc <- max(c(x_neg, x_pos)) } return(data.frame(optimal_cutpoint = oc)) } youden_kern <- function(threshold = NULL, neg_k, pos_k) { youden <- vector_auc(neg_k$x, neg_k$y, to = threshold) - vector_auc(pos_k$x, pos_k$y, to = threshold) return(youden) } #' @source Forked from MESS vector_auc <- function (x, y, from = min(x), to = max(x), ...) { if (length(x) != length(y)) stop("x and y must have the same length") if (length(unique(x)) < 2) return(NA) if (to > max(x)) to <- max(x) values <- stats::approx(x, y, xout = sort(unique(c(from, to, x[x > from & x < to]))), ...) res <- 0.5 * sum(diff(values$x) * (values$y[-1] + values$y[-length(values$y)])) res } cutpointr/R/plot_sensitivity_specificity.R0000644000176200001440000000621514726066534020671 0ustar liggesusers#' Sensitivity and specificity plot from a cutpointr object #' #' Given a \code{cutpointr} object this function plots the sensitivity and specificity #' curve(s) per subgroup, if the latter is given. #' @param x A cutpointr object. #' @param display_cutpoint (logical) Whether or not to display the optimal #' cutpoint as a dot on the precision recall curve. #' @param ... Additional arguments (unused). #' @examples #' library(cutpointr) #' #' ## Optimal cutpoint for dsi #' data(suicide) #' opt_cut <- cutpointr(suicide, dsi, suicide) #' plot_sensitivity_specificity(opt_cut) #' @family cutpointr plotting functions #' @export #' @importFrom purrr %>% plot_sensitivity_specificity <- function(x, display_cutpoint = TRUE, ...) { if (!("cutpointr" %in% class(x))) { stop("Only cutpointr objects are supported.") } args <- list(...) if (!(has_column(x, "subgroup"))) { dts_pr <- c("roc_curve", "optimal_cutpoint") } else { dts_pr <- c("roc_curve", "subgroup", "optimal_cutpoint") } plot_title <- ggplot2::ggtitle("Sensitivity and Specificity Plot") for (r in 1:nrow(x)) { x$roc_curve[[r]] <- x$roc_curve[[r]] %>% dplyr::mutate(Sensitivity = tp / (tp + fn), Specificity = tn / (tn + fp)) } res_unnested <- x %>% dplyr::select(dts_pr) %>% tidyr::unnest(.data$roc_curve) res_unnested <- res_unnested[is.finite(res_unnested$x.sorted), ] res_unnested <- tidyr::gather(res_unnested, key = "metric", value = "value", Sensitivity, Specificity) if (!(has_column(x, "subgroup"))) { pr <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = x.sorted, y = value, color = metric)) } else { pr <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = x.sorted, y = value, color = metric, linetype = subgroup)) } pr <- pr + ggplot2::geom_line() + plot_title + ggplot2::xlab("Cutpoint") + ggplot2::ylab("Sensitivity and Specificity") if (display_cutpoint) { if (!(has_column(x, "subgroup"))) { res_cutpoints <- x %>% dplyr::select(.data$optimal_cutpoint) if (is.list(res_cutpoints$optimal_cutpoint)) { res_cutpoints <- tidyr::unnest(res_cutpoints) } pr <- pr + ggplot2::geom_vline(data = res_cutpoints, ggplot2::aes(xintercept = optimal_cutpoint)) } else { res_cutpoints <- x %>% dplyr::select(.data$optimal_cutpoint, .data$subgroup) if (is.list(res_cutpoints$optimal_cutpoint)) { res_cutpoints <- tidyr::unnest(res_cutpoints) } pr <- pr + ggplot2::geom_vline(data = res_cutpoints, ggplot2::aes(xintercept = optimal_cutpoint, linetype = subgroup)) } } return(pr) } cutpointr/R/oc_median.R0000644000176200001440000000155614726066534014567 0ustar liggesusers#' Use the sample median as cutpoint #' #' The sample median is calculated and returned as the optimal cutpoint. #' #' @param data A data frame or tibble in which the columns that are given in x #' and class can be found. #' @param x (character) The variable name to be used for classification, #' e.g. predictions or test values. #' @param ... To capture further arguments that are always passed to the method #' function by cutpointr. The cutpointr function passes data, x, class, #' metric_func, direction, pos_class and neg_class to the method function. #' @examples #' data(suicide) #' oc_median(suicide, "dsi") #' cutpointr(suicide, dsi, suicide, method = oc_median) #' @family method functions #' @export oc_median <- function(data, x, ...) { stopifnot(is.character(x)) return(data.frame(optimal_cutpoint = stats::median(unlist(data[, x])))) }cutpointr/R/cutpointr.R0000644000176200001440000013420014726126565014672 0ustar liggesusers#' Determine and evaluate optimal cutpoints #' #' Using predictions (or e.g. biological marker values) and binary class labels, this function #' will determine "optimal" cutpoints using various selectable methods. The #' methods for cutpoint determination can be evaluated using bootstrapping. An #' estimate of the cutpoint variability and the out-of-sample performance can then #' be returned with \code{summary} or \code{plot}. For an introduction to the #' package please see \code{vignette("cutpointr", package = "cutpointr")}. #' #' If \code{direction} and/or \code{pos_class} and \code{neg_class} are not given, the function will #' assume that higher values indicate the positive class and use the class #' with a higher median as the positive class. #' #' This function uses tidyeval to support unquoted arguments. For programming #' with \code{cutpointr} the operator \code{!!} can be used to unquote an argument, see the #' examples. #' #' Different methods can be selected for determining the optimal cutpoint via #' the method argument. The package includes the following method functions: #' \itemize{ #' \item \code{maximize_metric}: Maximize the metric function #' \item \code{minimize_metric}: Minimize the metric function #' \item \code{maximize_loess_metric}: Maximize the metric function after LOESS #' smoothing #' \item \code{minimize_loess_metric}: Minimize the metric function after LOESS #' smoothing #' \item \code{maximize_boot_metric}: Maximize the metric function as a summary of #' the optimal cutpoints in bootstrapped samples #' \item \code{minimize_boot_metric}: Minimize the metric function as a summary of #' the optimal cutpoints in bootstrapped samples #' \item \code{oc_youden_kernel}: Maximize the Youden-Index after kernel smoothing #' the distributions of the two classes #' \item \code{oc_youden_normal}: Maximize the Youden-Index parametrically #' assuming normally distributed data in both classes #' \item \code{oc_manual}: Specify the cutpoint manually #' } #' #' User-defined functions can be supplied to method, too. As a reference, #' the code of all included method functions can be accessed by simply typing #' their name. To define a new method function, create a function that may take #' as input(s): #' \itemize{ #' \item \code{data}: A \code{data.frame} or \code{tbl_df} #' \item \code{x}: (character) The name of the predictor or independent variable #' \item \code{class}: (character) The name of the class or dependent variable #' \item \code{metric_func}: A function for calculating a metric, e.g. accuracy #' \item \code{pos_class}: The positive class #' \item \code{neg_class}: The negative class #' \item \code{direction}: ">=" if the positive class has higher x values, "<=" otherwise #' \item \code{tol_metric}: (numeric) In the built-in methods a tolerance around #' the optimal metric value #' \item \code{use_midpoints}: (logical) In the built-in methods whether to #' use midpoints instead of exact optimal cutpoints #' \item \code{...} Further arguments #' } #' #' The \code{...} argument can be used to avoid an error if not all of the above #' arguments are needed or in order to pass additional arguments to method. #' The function should return a \code{data.frame} or \code{tbl_df} with #' one row, the column "optimal_cutpoint", and an optional column with an arbitrary name #' with the metric value at the optimal cutpoint. #' #' Built-in metric functions include: #' \itemize{ #' \item \code{accuracy}: Fraction correctly classified #' \item \code{youden}: Youden- or J-Index = sensitivity + specificity - 1 #' \item \code{sum_sens_spec}: sensitivity + specificity #' \item \code{sum_ppv_npv}: The sum of positive predictive value (PPV) and negative #' predictive value (NPV) #' \item \code{prod_sens_spec}: sensitivity * specificity #' \item \code{prod_ppv_npv}: The product of positive predictive value (PPV) and #' negative predictive value (NPV) #' \item \code{cohens_kappa}: Cohen's Kappa #' \item \code{abs_d_sens_spec}: The absolute difference between #' sensitivity and specificity #' \item \code{roc01}: Distance to the point (0,1) on ROC space #' \item \code{abs_d_ppv_npv}: The absolute difference between positive predictive #' value (PPV) and negative predictive value (NPV) #' \item \code{p_chisquared}: The p-value of a chi-squared test on the confusion #' matrix of predictions and observations #' \item \code{odds_ratio}: The odds ratio calculated as (TP / FP) / (FN / TN) #' \item \code{risk_ratio}: The risk ratio (relative risk) calculated as #' (TP / (TP + FN)) / (FP / (FP + TN)) #' \item positive and negative likelihood ratio calculated as #' \code{plr} = true positive rate / false positive rate and #' \code{nlr} = false negative rate / true negative rate #' \item \code{misclassification_cost}: The sum of the misclassification cost of #' false positives and false negatives fp * cost_fp + fn * cost_fn. #' Additional arguments to cutpointr: \code{cost_fp}, \code{cost_fn} #' \item \code{total_utility}: The total utility of true / false positives / negatives #' calculated as utility_tp * TP + utility_tn * TN - cost_fp * FP - cost_fn * FN. #' Additional arguments to cutpointr: \code{utility_tp}, \code{utility_tn}, #' \code{cost_fp}, \code{cost_fn} #' \item \code{F1_score}: The F1-score (2 * TP) / (2 * TP + FP + FN) #' \item \code{sens_constrain}: Maximize sensitivity given a minimal value of #' specificity #' \item \code{spec_constrain}: Maximize specificity given a minimal value of #' sensitivity #' \item \code{metric_constrain}: Maximize a selected metric given a minimal #' value of another selected metric #' } #' #' Furthermore, the following functions are included which can be used as metric #' functions but are more useful for plotting purposes, for example in #' plot_cutpointr, or for defining new metric functions: #' \code{tp}, \code{fp}, \code{tn}, \code{fn}, \code{tpr}, \code{fpr}, #' \code{tnr}, \code{fnr}, \code{false_omission_rate}, #' \code{false_discovery_rate}, \code{ppv}, \code{npv}, \code{precision}, #' \code{recall}, \code{sensitivity}, and \code{specificity}. #' #' User defined metric functions can be created as well which can accept the following #' inputs as vectors: #' \itemize{ #' \item \code{tp}: Vector of true positives #' \item \code{fp}: Vector of false positives #' \item \code{tn}: Vector of true negatives #' \item \code{fn}: Vector of false negatives #' \item \code{...} If the metric function is used in conjunction with any of the #' maximize / minimize methods, further arguments can be passed #' } #' #' The function should return a numeric vector or a matrix or a \code{data.frame} #' with one column. If the column is named, #' the name will be included in the output and plots. Avoid using names that #' are identical to the column names that are by default returned by \pkg{cutpointr}. #' #' If \code{boot_runs} is positive, that number of bootstrap samples will be drawn #' and the optimal cutpoint using \code{method} will be determined. Additionally, #' as a way of internal validation, the function in \code{metric} will be used to #' score the out-of-bag predictions using the cutpoints determined by #' \code{method}. Various default metrics are always included in the bootstrap results. #' #' If multiple optimal cutpoints are found, the column optimal_cutpoint becomes a #' list that contains the vector(s) of the optimal cutpoints. #' #' If \code{use_midpoints = TRUE} the mean of the optimal cutpoint and the next #' highest or lowest possible cutpoint is returned, depending on \code{direction}. #' #' The \code{tol_metric} argument can be used to avoid floating-point problems #' that may lead to exclusion of cutpoints that achieve the optimally achievable #' metric value. Additionally, by selecting a large tolerance multiple cutpoints #' can be returned that lead to decent metric values in the vicinity of the #' optimal metric value. \code{tol_metric} is passed to metric and is only #' supported by the maximization and minimization functions, i.e. #' \code{maximize_metric}, \code{minimize_metric}, \code{maximize_loess_metric}, #' \code{minimize_loess_metric}. In \code{maximize_boot_metric} and #' \code{minimize_boot_metric} multiple optimal cutpoints will be passed to the #' \code{summary_func} of these two functions. #' #' @examples #' library(cutpointr) #' #' ## Optimal cutpoint for dsi #' data(suicide) #' opt_cut <- cutpointr(suicide, dsi, suicide) #' opt_cut #' s_opt_cut <- summary(opt_cut) #' plot(opt_cut) #' #' \dontrun{ #' ## Predict class for new observations #' predict(opt_cut, newdata = data.frame(dsi = 0:5)) #' #' ## Supplying raw data, same result #' cutpointr(x = suicide$dsi, class = suicide$suicide) #' #' ## direction, class labels, method and metric can be defined manually #' ## Again, same result #' cutpointr(suicide, dsi, suicide, direction = ">=", pos_class = "yes", #' method = maximize_metric, metric = youden) #' #' ## Optimal cutpoint for dsi, as before, but for the separate subgroups #' opt_cut <- cutpointr(suicide, dsi, suicide, gender) #' opt_cut #' (s_opt_cut <- summary(opt_cut)) #' tibble:::print.tbl(s_opt_cut) #' #' ## Bootstrapping also works on individual subgroups #' set.seed(30) #' opt_cut <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 1000, #' boot_stratify = TRUE) #' opt_cut #' summary(opt_cut) #' plot(opt_cut) #' #' ## Parallelized bootstrapping #' library(doParallel) #' library(doRNG) #' cl <- makeCluster(2) # 2 cores #' registerDoParallel(cl) #' registerDoRNG(12) # Reproducible parallel loops using doRNG #' opt_cut <- cutpointr(suicide, dsi, suicide, gender, #' boot_runs = 1000, allowParallel = TRUE) #' stopCluster(cl) #' opt_cut #' plot(opt_cut) #' #' ## Robust cutpoint method using kernel smoothing for optimizing Youden-Index #' opt_cut <- cutpointr(suicide, dsi, suicide, gender, #' method = oc_youden_kernel) #' opt_cut #' } #' #' #' #' @param data A data.frame with the data needed for x, class and optionally #' subgroup. #' @param x The variable name to be used for classification, #' e.g. predictions. The raw vector of values if the data argument #' is unused. #' @param class The variable name indicating class membership. #' If the data argument is unused, the vector of raw numeric values. #' @param subgroup An additional covariate that identifies subgroups or the raw data if #' data = NULL. Separate optimal cutpoints will be determined per group. #' Numeric, character and factor are allowed. #' @param method (function) A function for determining cutpoints. Can #' be user supplied or use some of the built in methods. See details. #' @param metric (function) The function for computing a metric when using #' maximize_metric or minimize_metric as method and and for the #' out-of-bag values during bootstrapping. A way of internally validating the performance. #' User defined functions can be supplied, see details. #' @param pos_class (optional) The value of class that indicates the positive class. #' @param neg_class (optional) The value of class that indicates the negative class. #' @param direction (character, optional) Use ">=" or "<=" to indicate whether x #' is supposed to be larger or smaller for the positive class. #' @param boot_runs (numerical) If positive, this number of bootstrap samples #' will be used to assess the variability and the out-of-sample performance. #' @param boot_stratify (logical) If the bootstrap is stratified, bootstrap #' samples are drawn separately in both classes and then combined, keeping the #' proportion of positives and negatives constant in every resample. #' @param use_midpoints (logical) If TRUE (default FALSE) the returned optimal #' cutpoint will be the mean of the optimal cutpoint and the next highest #' observation (for direction = ">=") or the next lowest observation #' (for direction = "<=") which avoids biasing the optimal cutpoint. #' @param break_ties If multiple cutpoints are found, they can be summarized using #' this function, e.g. mean or median. To return all cutpoints use c as the function. #' @param na.rm (logical) Set to TRUE (default FALSE) to keep only complete #' cases of x, class and subgroup (if specified). Missing values with #' na.rm = FALSE will raise an error. #' @param allowParallel (logical) If TRUE, the bootstrapping will be parallelized #' using foreach. A local cluster, for example, should be started manually #' beforehand. #' @param silent (logical) If TRUE suppresses all messages. #' @param tol_metric All cutpoints will be returned that lead to a metric #' value in the interval [m_max - tol_metric, m_max + tol_metric] where #' m_max is the maximum achievable metric value. This can be used to return #' multiple decent cutpoints and to avoid floating-point problems. Not supported #' by all \code{method} functions, see details. #' @param ... Further optional arguments that will be passed to method. #' minimize_metric and maximize_metric pass ... to metric. #' @importFrom purrr %>% #' @importFrom foreach %do% #' @return A cutpointr object which is also a data.frame and tbl_df. #' @useDynLib cutpointr #' @importFrom Rcpp sourceCpp #' @family main cutpointr functions #' @export cutpointr cutpointr <- function(...) { UseMethod("cutpointr") } #' @rdname cutpointr #' @importFrom stats median #' @export cutpointr.default <- function(data, x, class, subgroup = NULL, method = maximize_metric, metric = sum_sens_spec, pos_class = NULL, neg_class = NULL, direction = NULL, boot_runs = 0, boot_stratify = FALSE, use_midpoints = FALSE, break_ties = median, na.rm = FALSE, allowParallel = FALSE, silent = FALSE, tol_metric = 1e-6, ...) { # # NSE # predictor <- rlang::as_name(rlang::enquo(x)) x_sym <- rlang::ensym(x) x_expr <- rlang::enexpr(x_sym) x <- rlang::eval_tidy(expr = x_expr, data = data) outcome <- rlang::as_name(rlang::enquo(class)) class_sym <- rlang::ensym(class) class_expr <- rlang::enexpr(class_sym) class <- rlang::eval_tidy(expr = class_expr, data = data) subgroup_var <- deparse(substitute(subgroup)) if (!(deparse(substitute(subgroup)) == "NULL")) { subgroup_var <- rlang::as_name(rlang::enquo(subgroup)) subgroup_sym <- rlang::ensym(subgroup) subgroup_expr <- rlang::enexpr(subgroup_sym) subgroup <- rlang::eval_tidy(expr = subgroup_expr, data = data) } # Get method function if (length(method) > 1 | !inherits(method, "function")) { stop("method should be a function") } else { cl <- match.call() mod_name <- cl$method # if default was not changed: mod_name <- as.character(substitute(method)) } if (is.null(mod_name)) stop("Could not get the method function") mod_name <- check_method_name(mod_name) # Get metric function if (length(metric) > 1 | !inherits(metric, "function")) { stop("metric should be a function") } if (silent) { suppressMessages( cutpointr_internal(x, class, subgroup, method, metric, pos_class, neg_class, direction, boot_runs, boot_stratify, use_midpoints, break_ties, na.rm, allowParallel, predictor, outcome, mod_name, subgroup_var, tol_metric, ...) ) } else { cutpointr_internal(x, class, subgroup, method, metric, pos_class, neg_class, direction, boot_runs, boot_stratify, use_midpoints, break_ties, na.rm, allowParallel, predictor, outcome, mod_name, subgroup_var, tol_metric, ...) } } #' @rdname cutpointr #' @importFrom stats median #' @export cutpointr.numeric <- function(x, class, subgroup = NULL, method = maximize_metric, metric = sum_sens_spec, pos_class = NULL, neg_class = NULL, direction = NULL, boot_runs = 0, boot_stratify = FALSE, use_midpoints = FALSE, break_ties = median, na.rm = FALSE, allowParallel = FALSE, silent = FALSE, tol_metric = 1e-6, ...) { predictor <- "x" outcome <- "class" subgroup_var <- "subgroup" # Get method function if (length(method) > 1 | !inherits(method, "function")) { stop("method should be a function") } else { cl <- match.call() mod_name <- cl$method # if default was not changed: mod_name <- as.character(substitute(method)) } if (is.null(mod_name)) stop("Could not get the method function") mod_name <- check_method_name(mod_name) # Get metric function if (length(metric) > 1 | !inherits(metric, "function")) { stop("metric should be a function") } if (silent) { suppressMessages( cutpointr_internal(x, class, subgroup, method, metric, pos_class, neg_class, direction, boot_runs, boot_stratify, use_midpoints, break_ties, na.rm, allowParallel, predictor, outcome, mod_name, subgroup_var, tol_metric, ...) ) } else { cutpointr_internal(x, class, subgroup, method, metric, pos_class, neg_class, direction, boot_runs, boot_stratify, use_midpoints, break_ties, na.rm, allowParallel, predictor, outcome, mod_name, subgroup_var, tol_metric, ...) } } #' The standard evaluation version of cutpointr (deprecated) #' #' This function is equivalent to \code{cutpointr} but takes only quoted arguments #' for \code{x}, \code{class} and \code{subgroup}. This was useful before #' \code{cutpointr} supported tidyeval. #' #' @inheritParams cutpointr #' @param x (character) The variable name to be used for #' classification, e.g. predictions or test values. #' @param class (character) The variable name indicating class membership. #' @param subgroup (character) The variable name #' of an additional covariate that identifies subgroups. Separate #' optimal cutpoints will be determined per group. #' @examples #' library(cutpointr) #' #' ## Optimal cutpoint for dsi #' data(suicide) #' opt_cut <- cutpointr_(suicide, "dsi", "suicide") #' opt_cut #' summary(opt_cut) #' plot(opt_cut) #' predict(opt_cut, newdata = data.frame(dsi = 0:5)) #' @importFrom stats median #' @export cutpointr_ <- function(data, x, class, subgroup = NULL, method = maximize_metric, metric = sum_sens_spec, pos_class = NULL, neg_class = NULL, direction = NULL, boot_runs = 0, boot_stratify = FALSE, use_midpoints = FALSE, break_ties = median, na.rm = FALSE, allowParallel = FALSE, silent = FALSE, tol_metric = 1e-6, ...) { signal_soft_deprecated(paste( "cutpointr_() is deprecated.", "Please use cutpointr() instead.", "The help page gives an example on programming with cutpointr", "using quoted arguments. For general information see the tidyeval book", ": https://tidyeval.tidyverse.org" )) # # SE # x <- as.name(x) class <- as.name(class) if (!is.null(subgroup)) subgroup <- as.name(subgroup) predictor <- deparse(substitute(x)) outcome <- deparse(substitute(class)) x <- eval(substitute(x), data, parent.frame()) class <- eval(substitute(class), data, parent.frame()) subgroup_var <- deparse(substitute(subgroup)) subgroup <- eval(substitute(subgroup), data, parent.frame()) # Get method function if (length(method) > 1 | !inherits(method, "function")) { stop("method should be a function") } else { cl <- match.call() mod_name <- cl$method mod_name <- as.character(substitute(method)) } if (is.null(mod_name)) stop("Could not get the method function") mod_name <- check_method_name(mod_name) # Get metric function if (length(metric) > 1 | !inherits(metric, "function")) { stop("metric should be a function") } if (silent) { suppressMessages( cutpointr_internal(x, class, subgroup, method, metric, pos_class, neg_class, direction, boot_runs, boot_stratify, use_midpoints, break_ties, na.rm, allowParallel, predictor, outcome, mod_name, subgroup_var, tol_metric = 1e-6, ...) ) } else { cutpointr_internal(x, class, subgroup, method, metric, pos_class, neg_class, direction, boot_runs, boot_stratify, use_midpoints, break_ties, na.rm, allowParallel, predictor, outcome, mod_name, subgroup_var, tol_metric = 1e-6, ...) } } cutpointr_internal <- function(x, class, subgroup, method, metric, pos_class, neg_class, direction, boot_runs, boot_stratify, use_midpoints, break_ties, na.rm, allowParallel, predictor, outcome, mod_name, subgroup_var, tol_metric, ...) { # # Prep # #NA if (any(anyNA(c(x, class)) | (!is.null(subgroup) & anyNA(subgroup))) & (!na.rm)) { stop("NAs found but na.rm = FALSE") } # Check classes if (!is.null(dim(class))) stop("class variable should be a vector") if (na.rm) uc <- unique(stats::na.omit(class)) else uc <- unique(class) luc <- length(uc) if (luc != 2) stop(paste("Expecting two classes, got", luc)) if (!(is.null(pos_class))) { if (!(pos_class %in% class)) stop("pos_class not found in data") } if (!(is.null(neg_class))) { if (!(neg_class %in% class)) stop("neg_class not found in data") } # Determine direction and/or pos_class if necessary: if (any(c(is.null(pos_class), is.null(neg_class), is.null(direction)))) { assumptions <- assume_direction_pos_class(x = x, class = class, pos_class = pos_class, neg_class = neg_class, direction = direction, na.rm = na.rm, uc = uc) } if (is.null(direction)) direction <- assumptions$direction stopifnot(direction %in% c("<", ">", ">=", "<=")) if (is.null(pos_class)) pos_class <- assumptions$pos_class if (is.null(neg_class)) neg_class <- assumptions$neg_class # # Calculate optimal cutpoint, map to cutpoint function # if (!is.null(subgroup)) { dat <- data.frame(x = x, class = class, subgroup = subgroup, stringsAsFactors = FALSE) colnames(dat) <- c(predictor, outcome, "subgroup") if (na.rm) dat <- stats::na.omit(dat) g <- unique(dat$subgroup) dat <- dat %>% dplyr::mutate(subgroup = as.character(subgroup)) %>% dplyr::group_by(subgroup) %>% tidyr::nest(data = c(!!predictor, !!outcome)) dat$pos_class <- pos_class optcut <- purrr::pmap(list(dat$subgroup, dat$data), function(g, d) { if (nrow(d) <= 1) stop(paste("Subgroup", g, "has <= 1 observations")) optcut <- tibble::tibble(subgroup = g) method_result <- method(data = d, x = predictor, class = outcome, metric_func = metric, direction = direction, pos_class = pos_class, neg_class = neg_class, tol_metric = tol_metric, use_midpoints = use_midpoints, boot_stratify = boot_stratify, ...) method_result <- check_method_cols(method_result) optcut <- dplyr::bind_cols(optcut, method_result) if (length(optcut[["optimal_cutpoint"]][[1]]) > 1) { message("Multiple optimal cutpoints found, applying break_ties.") } optcut <- apply_break_ties(optcut, break_ties) # Depending on method the roc_curve may be missing if (!(has_column(optcut, "roc_curve"))) { roc_curve <- roc(data = d, x = !!predictor, class = !!outcome, pos_class = pos_class, neg_class = neg_class, direction = direction) # roc_curve <- tidyr::nest(.data = tibble::as_tibble(roc_curve), # roc_curve = dplyr::everything()) %>% # tibble::as_tibble() roc_curve <- tibble::tibble(roc_curve = list(roc_curve)) optcut <- dplyr::bind_cols(tibble::as_tibble(roc_curve), tibble::as_tibble(optcut)) } else { check_roc_curve(optcut) } # If no metric is returned if (ncol(optcut) <= 3) { opt_ind <- get_opt_ind(optcut$roc_curve[[1]], oc = unlist(optcut$optimal_cutpoint), direction = direction) m <- metric(tp = optcut$roc_curve[[1]]$tp[opt_ind], fp = optcut$roc_curve[[1]]$fp[opt_ind], tn = optcut$roc_curve[[1]]$tn[opt_ind], fn = optcut$roc_curve[[1]]$fn[opt_ind], .roc_curve = optcut$roc_curve[[1]], ...) m <- check_metric_name(m) colnames(m) <- make.names(colnames(m)) optcut <- dplyr::bind_cols(optcut, tibble::as_tibble(m)) } optcut <- check_colnames(optcut) # Breaking ties may have altered the cutpoints. Recalculate main metric opt_ind <- get_opt_ind(optcut$roc_curve[[1]], oc = unlist(optcut$optimal_cutpoint), direction = direction) m <- metric(tp = optcut$roc_curve[[1]]$tp[opt_ind], fp = optcut$roc_curve[[1]]$fp[opt_ind], tn = optcut$roc_curve[[1]]$tn[opt_ind], fn = optcut$roc_curve[[1]]$fn[opt_ind], .roc_curve = optcut$roc_curve[[1]], ...) optcut <- add_list(optcut, as.numeric(m), optcut$metric_name) sesp <- sesp_from_oc(optcut$roc_curve[[1]], oc = optcut$optimal_cutpoint, direction = direction) optcut <- add_list(optcut, sesp[, "sensitivity"], "sensitivity") optcut <- add_list(optcut, sesp[, "specificity"], "specificity") acc <- accuracy_from_oc(optcut$roc_curve[[1]], oc = optcut$optimal_cutpoint[[1]], direction = direction)[, "accuracy"] optcut <- add_list(optcut, acc, "acc") return(optcut) }) # if multiple cutpoints only in some groups, all cols have to be lists coltypes <- purrr::map_chr(optcut, function(x) class(x[2][[1]])) if (any(coltypes == "list") & any(coltypes == "numeric")) { optcut <- purrr::map(optcut, function(x) { if (is.numeric(x$optimal_cutpoint)) { x$optimal_cutpoint <- list(x$optimal_cutpoint) x[[x$metric_name]] <- list(x[[x$metric_name]]) x$sensitivity <- list(x$sensitivity) x$specificity <- list(x$specificity) x$acc <- list(x$acc) } return(x) }) } # Suppress "Vectorizing 'vctrs_list_of' elements may not preserve their attributes" optcut <- suppressWarnings(dplyr::bind_rows(optcut)) optcut <- optcut %>% dplyr::mutate( AUC = purrr::map_dbl(roc_curve, function(r) { auc(r) }), prevalence = purrr::map_dbl(roc_curve, function(r) { utils::tail(r$tp, 1) / (utils::tail(r$tp, 1) + utils::tail(r$fp, 1)) }) ) optcut <- tibble::as_tibble(optcut) optcut <- dplyr::full_join(optcut, dat, by = "subgroup") } else if (is.null(subgroup)) { dat <- tibble::tibble(x = x, class = class) colnames(dat) <- c(predictor, outcome) if (na.rm) dat <- stats::na.omit(dat) dat <- dat %>% tidyr::nest(data = c(!!predictor, !!outcome)) dat$pos_class <- pos_class optcut <- method(data = dat$data[[1]], x = predictor, class = outcome, metric_func = metric, direction = direction, pos_class = pos_class, neg_class = neg_class, tol_metric = tol_metric, boot_stratify = boot_stratify, use_midpoints = use_midpoints, ...) optcut <- check_method_cols(optcut) if (length(optcut[["optimal_cutpoint"]][[1]]) > 1) { message("Multiple optimal cutpoints found, applying break_ties.") } optcut <- apply_break_ties(optcut, break_ties) if (!(has_column(optcut, "roc_curve"))) { roc_curve <- roc(data = dat$data[[1]], x = !!predictor, class = !!outcome, pos_class = pos_class, neg_class = neg_class, direction = direction) roc_curve <- tibble::tibble(roc_curve = list(roc_curve)) optcut <- dplyr::bind_cols(roc_curve, tibble::as_tibble(optcut)) } else { check_roc_curve(optcut) } # If no metric is returned if (ncol(optcut) <= 2) { opt_ind <- get_opt_ind(optcut$roc_curve[[1]], oc = unlist(optcut$optimal_cutpoint), direction = direction) m <- metric(tp = optcut$roc_curve[[1]]$tp[opt_ind], fp = optcut$roc_curve[[1]]$fp[opt_ind], tn = optcut$roc_curve[[1]]$tn[opt_ind], fn = optcut$roc_curve[[1]]$fn[opt_ind], .roc_curve = optcut$roc_curve[[1]], ...) m <- check_metric_name(m) colnames(m) <- make.names(colnames(m)) optcut <- dplyr::bind_cols(optcut, tibble::as_tibble(m)) } optcut <- check_colnames(optcut) # Breaking ties may have altered the cutpoints. Recalculate main metric opt_ind <- get_opt_ind(optcut$roc_curve[[1]], oc = unlist(optcut$optimal_cutpoint), direction = direction) m <- metric(tp = optcut$roc_curve[[1]]$tp[opt_ind], fp = optcut$roc_curve[[1]]$fp[opt_ind], tn = optcut$roc_curve[[1]]$tn[opt_ind], fn = optcut$roc_curve[[1]]$fn[opt_ind], .roc_curve = optcut$roc_curve[[1]], ...) optcut <- add_list(optcut, as.numeric(m), optcut$metric_name) sesp <- sesp_from_oc(optcut$roc_curve[[1]], oc = optcut$optimal_cutpoint, direction = direction) optcut <- add_list(optcut, sesp[, "sensitivity"], "sensitivity") optcut <- add_list(optcut, sesp[, "specificity"], "specificity") acc <- accuracy_from_oc(optcut$roc_curve[[1]], oc = unlist(optcut$optimal_cutpoint), direction = direction)[, "accuracy"] optcut <- add_list(optcut, acc, "acc") optcut$AUC <- auc(optcut$roc_curve[[1]]) optcut$prevalence <- utils::tail(optcut$roc_curve[[1]]$tp, 1) / (utils::tail(optcut$roc_curve[[1]]$tp, 1) + utils::tail(optcut$roc_curve[[1]]$fp, 1)) optcut <- tibble::as_tibble(optcut) optcut <- dplyr::bind_cols(optcut, dat) } optcut$direction <- direction optcut$predictor <- predictor optcut$outcome <- outcome optcut$neg_class <- neg_class optcut$method <- mod_name if (!is.null(subgroup)) optcut$grouping <- subgroup_var # Reorder for nicer output mn <- optcut$metric_name[1] select_cols <- c("subgroup", "direction", "optimal_cutpoint", "method", mn, "acc", "sensitivity", "specificity", "AUC", "pos_class", "neg_class", "prevalence", "outcome", "predictor", "grouping", "data", "roc_curve") # subgroup and grouping may not be given select_cols <- select_cols[select_cols %in% colnames(optcut)] optcut <- optcut[, select_cols] # # Bootstrap cutpoint variability and get LOO-Bootstrap performance estimate # Data are already nested and grouped if necessary # if (allowParallel) { requireNamespace("foreach") `%seq_or_par%` <- doRNG::`%dorng%` } else { `%seq_or_par%` <- foreach::`%do%` } if (boot_runs <= 0) { boot_res <- rep(NA, times = nrow(optcut)) } else { message("Running bootstrap...") boot_runs <- ceiling(boot_runs) boot_res <- purrr::map2(dat$data, dat$pos_class, function(g, pc) { if (boot_stratify) { ind_pos <- which(unlist(g[, outcome]) == pc) ind_neg <- which(unlist(g[, outcome]) == neg_class) } else { ind_pos <- NA ind_neg <- NA } boot_g <- foreach::foreach(rep = 1:boot_runs, .export = c("method", "direction", "metric", "break_ties", "neg_class", "mn", "use_midpoints", "boot_stratify", "predictor", "outcome", "tol_metric"), .errorhandling = "remove") %seq_or_par% { b_ind <- simple_boot(data = g, dep_var = outcome, ind_pos = ind_pos, ind_neg = ind_neg, stratify = boot_stratify) optcut_b <- method(data = g[b_ind, ], x = predictor, metric_func = metric, class = outcome, direction = direction, pos_class = pc, neg_class = neg_class, tol_metric = tol_metric, boot_stratify = boot_stratify, use_midpoints = use_midpoints, ...) optcut_b <- check_method_cols(optcut_b) optcut_b <- tibble::as_tibble(optcut_b) optcut_b <- apply_break_ties(optcut_b, break_ties) # LOO-Bootstrap if (!(has_column(optcut_b, "roc_curve"))) { roc_curve_b <- roc(data = g[b_ind, ], x = !!predictor, class = !!outcome, pos_class = pc, neg_class = neg_class, direction = direction, silent = TRUE) roc_curve_b <- tibble::tibble(roc_curve = list(roc_curve_b)) optcut_b <- dplyr::bind_cols(tibble::as_tibble(optcut_b), roc_curve_b) } opt_ind_b <- get_opt_ind(roc_curve = optcut_b$roc_curve[[1]], oc = unlist(optcut_b$optimal_cutpoint), direction = direction) auc_b <- auc(optcut_b$roc_curve[[1]]) Sens_Spec_b <- sesp_from_oc(optcut_b$roc_curve[[1]], oc = unlist(optcut_b$optimal_cutpoint), direction = direction, opt_ind = opt_ind_b) Acc_b <- accuracy_from_oc(optcut_b$roc_curve[[1]], oc = unlist(optcut_b$optimal_cutpoint), direction = direction, opt_ind = opt_ind_b)[, "accuracy"] kap_b <- kappa_from_oc(optcut_b$roc_curve[[1]], oc = unlist(optcut_b$optimal_cutpoint), direction = direction, opt_ind = opt_ind_b) metric_b <- metric(tp = optcut_b$roc_curve[[1]]$tp[opt_ind_b], fp = optcut_b$roc_curve[[1]]$fp[opt_ind_b], tn = optcut_b$roc_curve[[1]]$tn[opt_ind_b], fn = optcut_b$roc_curve[[1]]$fn[opt_ind_b], .roc_curve = optcut_b$roc_curve[[1]], ...) metric_b <- check_metric_name(metric_b) roc_curve_oob <- roc(data = g[-b_ind, ], x = !!predictor, class = !!outcome, pos_class = pc, neg_class = neg_class, direction = direction, silent = TRUE) opt_ind_oob <- get_opt_ind(roc_curve = roc_curve_oob, oc = unlist(optcut_b$optimal_cutpoint), direction = direction) auc_oob <- auc(roc_curve_oob) Sens_Spec_oob <- sesp_from_oc(roc_curve_oob, oc = unlist(optcut_b$optimal_cutpoint), direction = direction, opt_ind = opt_ind_oob) Acc_oob <- accuracy_from_oc(roc_curve_oob, oc = unlist(optcut_b$optimal_cutpoint), direction = direction, opt_ind = opt_ind_oob)[, "accuracy"] kap_oob <- kappa_from_oc(roc_curve_oob, oc = unlist(optcut_b$optimal_cutpoint), direction = direction, opt_ind = opt_ind_oob) metric_oob <- metric(tp = roc_curve_oob$tp[opt_ind_oob], fp = roc_curve_oob$fp[opt_ind_oob], tn = roc_curve_oob$tn[opt_ind_oob], fn = roc_curve_oob$fn[opt_ind_oob], .roc_curve = roc_curve_oob, ...) metric_oob <- check_metric_name(metric_oob) mn <- make.names(colnames(metric_oob)) bootstrap <- tibble::tibble( optimal_cutpoint = optcut_b$optimal_cutpoint, AUC_b = auc_b, AUC_oob = auc_oob ) bootstrap <- bootstrap %>% add_list(metric_b[, mn], paste0(mn, "_b")) %>% add_list(metric_oob[, mn], paste0(mn, "_oob")) %>% add_list(Acc_b, "acc_b") %>% add_list(Acc_oob, "acc_oob") %>% add_list(Sens_Spec_b[, "sensitivity"], "sensitivity_b") %>% add_list(Sens_Spec_oob[, "sensitivity"], "sensitivity_oob") %>% add_list(Sens_Spec_b[, "specificity"], "specificity_b") %>% add_list(Sens_Spec_oob[, "specificity"], "specificity_oob") %>% add_list(kap_b[, "cohens_kappa"], "cohens_kappa_b") %>% add_list(kap_oob[, "cohens_kappa"], "cohens_kappa_oob") %>% add_list(optcut_b$roc_curve[[1]]$tp[opt_ind_b], "TP_b") %>% add_list(optcut_b$roc_curve[[1]]$fp[opt_ind_b], "FP_b") %>% add_list(optcut_b$roc_curve[[1]]$tn[opt_ind_b], "TN_b") %>% add_list(optcut_b$roc_curve[[1]]$fn[opt_ind_b], "FN_b") %>% add_list(roc_curve_oob$tp[opt_ind_oob], "TP_oob") %>% add_list(roc_curve_oob$fp[opt_ind_oob], "FP_oob") %>% add_list(roc_curve_oob$tn[opt_ind_oob], "TN_oob") %>% add_list(roc_curve_oob$fn[opt_ind_oob], "FN_oob") bootstrap$roc_curve_b = optcut_b$roc_curve roc_curve_oob <- tibble::tibble(roc_curve_oob = list(roc_curve_oob)) bootstrap <- dplyr::bind_cols(bootstrap, tibble::as_tibble(roc_curve_oob)) return(bootstrap) } boot_g <- prepare_bind_rows(boot_g) boot_g <- suppressWarnings(dplyr::bind_rows(boot_g)) return(boot_g) }) missing_reps <- purrr::map_lgl(.x = boot_res, .f = function(x) nrow(x) != boot_runs) if (any(missing_reps)) { # Some repetitions may be missing even when boot_stratify = TRUE # when there's only one class in the out-of-bag data message(paste("Some bootstrap repetitions were removed because", "of missing values or errors.")) } } optcut$boot = boot_res res <- optcut class(res) <- c("cutpointr", class(res)) return(res) } #' Calculate optimal cutpoints and further statistics for multiple predictors #' #' Runs \code{cutpointr} over multiple predictor variables. Tidyeval via #' \code{!!} is supported for \code{class} and \code{subgroup}. If #' \code{x = NULL}, \code{cutpointr} will be run using all numeric columns #' in the data set as predictors except for the #' variable in \code{class} and, if given, \code{subgroup}. #' #' The automatic determination of positive / negative classes and \code{direction} #' will be carried out separately for every predictor variable. That way, if #' \code{direction} and the classes are not specified, the reported AUC for every #' variable will be >= 0.5. AUC may be < 0.5 if subgroups are specified as #' \code{direction} is equal within every subgroup. #' #' @param data A data frame. #' @param x Character vector of predictor variables. If NULL all numeric columns. #' @param class The name of the outcome / independent variable. #' @param subgroup An additional covariate that identifies subgroups. Separate #' optimal cutpoints will be determined per group. #' @param silent Whether to suppress messages. #' @param ... Further arguments to be passed to cutpointr, e.g., boot_runs #' @examples #' library(cutpointr) #' #' multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide, #' pos_class = "yes") #' #' mcp <- multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide, #' subgroup = gender, pos_class = "yes") #' mcp #' #' (scp <- summary(mcp)) #' \dontrun{ #' ## The result is a data frame #' tibble:::print.tbl(scp) #' } #' #' @return A data frame. #' @importFrom purrr %>% #' @family main cutpointr functions #' @export multi_cutpointr <- function(data, x = NULL, class, subgroup = NULL, silent = FALSE, ...) { if (rlang::as_label(rlang::enquo(subgroup)) == "NULL") { subgroup_lab <- "NULL" } else { subgroup_sym <- rlang::ensym(subgroup) subgroup_lab <- rlang::as_label(subgroup_sym) } if (subgroup_lab != "NULL") { if (subgroup_lab %in% c("TRUE", "FALSE", "T", "F")) { stop(paste("The arguments to multi_cutpointr", "have changed. Please see ?multi_cutpointr")) } } class_sym <- rlang::ensym(class) class_lab <- rlang::as_label(class_sym) if (is.null(x)) x = get_numeric_cols(data, class_lab) if (subgroup_lab != "NULL") { x <- x[x != subgroup_lab] } if (subgroup_lab == "NULL") { res <- purrr::map(x, function(coln) { if (!silent) message(paste0(coln, ":")) cutpointr(data, !!coln, !!class_lab, silent = silent, ...) }) } else { res <- purrr::map(x, function(coln) { if (!silent) message(paste0(coln, ":")) cutpointr(data, !!coln, !!class_lab, subgroup = !!subgroup_lab, silent = silent, ...) }) } res <- suppressWarnings(dplyr::bind_rows(res)) base::class(res) <- c("multi_cutpointr", base::class(res)[-which(base::class(res) == "cutpointr")]) return(res) }cutpointr/R/summary.multi_cutpointr.R0000644000176200001440000000671014726066534017603 0ustar liggesusers#' @export summary.multi_cutpointr <- function(object, ...) { x_summary <- vector("list", nrow(object)) names(x_summary) <- suppressWarnings(object$subgroup) for (r in 1:nrow(object)) { temprow <- object[r, ] if (has_column(object, "subgroup")) { x_summary[[r]]$subgroup <- temprow$subgroup } x_summary[[r]]$predictor <- temprow$predictor x_summary[[r]]$cutpointr <- temprow x_summary[[r]]$desc <- dplyr::bind_cols( tibble::tibble(Data = "Overall"), summary_sd_df(temprow$data[[1]] %>% dplyr::select(temprow$predictor)) ) temprow_split <- temprow$data[[1]] %>% split(temprow$data[[1]][, temprow$outcome]) x_summary[[r]]$desc_byclass <- temprow_split %>% purrr::map_df(function(x) summary_sd_df(x %>% dplyr::select(temprow$predictor))) x_summary[[r]]$desc_byclass <- dplyr::bind_cols( data.frame(Data = names(temprow_split), stringsAsFactors = FALSE), x_summary[[r]]$desc_byclass ) x_summary[[r]]$n_obs <- nrow(temprow$data[[1]]) x_summary[[r]]$n_pos <- temprow$data[[1]] %>% dplyr::select(!!as.name(temprow$outcome)) %>% unlist %>% (function(x) sum(x == temprow$pos_class)) x_summary[[r]]$n_neg <- x_summary[[r]]$n_obs - x_summary[[r]]$n_pos # Confusion Matrix oi <- get_opt_ind(temprow$roc_curve[[1]], oc = unlist(temprow$optimal_cutpoint), direction = temprow$direction) x_summary[[r]]$confusion_matrix <- data.frame( cutpoint = unlist(temprow$optimal_cutpoint), temprow$roc_curve[[1]][oi, c("tp", "fn", "fp", "tn")] ) if (has_boot_results(temprow)) { x_summary[[r]][["boot"]] <- purrr::map(temprow[["boot"]][[1]][, 1:13], function(x) { summary_sd(x) }) x_summary[[r]][["boot"]] <- do.call(rbind, x_summary[[r]][["boot"]]) x_summary[[r]][["boot"]] <- as.data.frame(x_summary[[r]][["boot"]]) x_summary[[r]][["boot"]] <- tibble::rownames_to_column(x_summary[[r]][["boot"]], var = "Variable") x_summary[[r]]$boot_runs <- nrow(temprow[["boot"]][[1]]) } } x_summary <- suppressMessages(purrr::map_df(x_summary, tidy_summary_multi)) class(x_summary) <- c("summary_multi_cutpointr", class(x_summary)) return(x_summary) } # Convert the list output of summary.cutpointr to a data.frame # x is a single element of the resulting list from summary.cutpointr. tidy_summary_multi <- function(x) { res <- tibble::tibble( predictor = x$predictor, cutpointr = list(x$cutpointr), desc = list(x$desc), desc_by_class = list(x$desc_byclass), n_obs = x$n_obs, n_pos = x$n_pos, n_neg = x$n_neg, confusion_matrix = list(x$confusion_matrix) ) if (has_boot_results(x)) { res <- dplyr::bind_cols(res, # tidyr::nest(x[["boot"]], .key = "boot"), tibble::tibble(boot = list(x[["boot"]])), boot_runs = x$boot_runs) } if (has_column(x, "subgroup")) { res <- dplyr::bind_cols(subgroup = x$subgroup, res) } return(res) } cutpointr/R/boot_test.R0000644000176200001440000002223014726066534014643 0ustar liggesusers#' Test for equivalence of a metric #' #' This function performs a significance test based on the bootstrap results #' of cutpointr to test whether a chosen metric is equal between subgroups #' or between two cutpointr objects. The test statistic is calculated as #' the standardized difference of the metric between groups. If \code{x} #' contains subgroups, the test is run on all possible pairings of subgroups. #' An additional adjusted p-value is returned in that case. #' #' The variable name is looked up in the columns of the bootstrap results #' where the suffixes _b and _oob indicate in-bag and out-of-bag estimates, #' respectively (controlled via the \code{in_bag} argument). #' Possible values are optimal_cutpoint, AUC, #' acc, sensitivity, specificity, and the metric that was selected #' in \code{cutpointr}. Note that there is no "out-of-bag optimal cutpoint", so #' when selecting \code{variable = optimal_cutpoint} the test will be based on #' the in-bag data. #' #' The test statistic is calculated as z = (t1 - t2) / sd(t1 - t2) where t1 and #' t2 are the metric values on the full sample and sd(t1 - t2) is the standard #' deviation of the differences of the metric values per bootstrap repetition. #' The test is two-sided. #' #' If two cutpointr objects are compared and the numbers of bootstrap repetitions #' differ, the smaller number will be used. #' #' Since pairwise differences are calculated for this test, the test function #' does not support multiple optimal cutpoints, because it is unclear how the #' differences should be calculated in that case. #' #' @param x A cutpointr object with bootstrap results #' @param y If x does not contain subgroups another cutpointr object #' @param variable The variable for testing #' @param in_bag Whether the in-bag or out-of-bag results should be used for testing #' @param correction The type of correction for multiple testing. Possible #' values are as in p.adjust.methods #' #' @return A data.frame (a tibble) with the columns test_var, p, d, sd_d, z #' and in_bag. If a grouped cutpointr object was tested, the additional #' columns subgroup1, subgroup2 and p_adj are returned. #' #' @examples #' \dontrun{ #' library(cutpointr) #' library(dplyr) #' set.seed(734) #' cp_f <- cutpointr(suicide %>% filter(gender == "female"), dsi, suicide, #' boot_runs = 1000, boot_stratify = TRUE) #' set.seed(928) #' cp_m <- cutpointr(suicide %>% filter(gender == "male"), dsi, suicide, #' boot_runs = 1000, boot_stratify = TRUE) #' # No significant differences: #' boot_test(cp_f, cp_m, AUC, in_bag = TRUE) #' boot_test(cp_f, cp_m, sum_sens_spec, in_bag = FALSE) #' #' set.seed(135) #' cp <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 1000, #' boot_stratify = TRUE) #' # Roughly same result as above: #' boot_test(cp, variable = AUC, in_bag = TRUE) #' boot_test(cp, variable = sum_sens_spec, in_bag = FALSE) #' } #' #' @export #' @family main cutpointr functions #' @source Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., #' Sanchez, J.-C., & Müller, M. (2011). pROC: An open-source package for R #' and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12(1), 77. #' https://doi.org/10.1186/1471-2105-12-77 boot_test <- function(x, y = NULL, variable = "AUC", in_bag = TRUE, correction = "holm") { if (!has_boot_results(x)) { stop(paste("x does not contain bootstrap results.", "Was boot_runs > 0 in cutpointr?")) } stopifnot(inherits(x, "cutpointr")) if (!is.null(y)) { if (!has_boot_results(y)) { stop(paste("y does not contain bootstrap results.", "Was boot_runs > 0 in cutpointr?")) } stopifnot(inherits(y, "cutpointr")) } # No boot_test if multiple cutpoints multi_cp_x <- purrr::map_lgl(x$boot, function(b) is.list(b$optimal_cutpoint)) if (!is.null(y)) { multi_cp_y <- purrr::map_lgl(y$boot, function(b) is.list(b$optimal_cutpoint)) } else { multi_cp_y <- FALSE } if (any(multi_cp_x) | any(multi_cp_y)) { stop(paste("boot_test does not support multiple optimal cutpoints.", "See ?boot_test. Try setting break_ties = median", "in cutpointr.")) } variable <- rlang::enquo(variable) variable <- rlang::as_name(variable) # If y is also supplied, x and y should be ungrouped if ((!is.null(y)) & nrow(x) > 1) { stop(paste("When testing two cutpointr objects these objects must not", "contain subgroups.")) } if (in_bag) suffix <- "_b" else suffix <- "_oob" if (variable == "optimal_cutpoint") suffix <- "" if (is.null(y)) { # Test subgroups vs. each other combs <- utils::combn(x = x$subgroup, m = 2, simplify = FALSE) combs <- do.call(rbind, combs) combs <- data.frame(var1 = combs[, 1], var2 = combs[, 2], stringsAsFactors = FALSE) test_res <- purrr::map2_dfr(.x = combs$var1, .y = combs$var2, .f = function(var1, var2) { dat_var1 <- x %>% dplyr::filter(.data$subgroup == var1) %>% dplyr::select(.data$boot) %>% tidyr::unnest(.data$boot) %>% dplyr::select(paste0(variable, suffix)) %>% unlist dat_var2 <- x %>% dplyr::filter(.data$subgroup == var2) %>% dplyr::select(.data$boot) %>% tidyr::unnest(.data$boot) %>% dplyr::select(paste0(variable, suffix)) %>% unlist na_v1 <- sum(is.na(dat_var1)) na_v2 <- sum(is.na(dat_var2)) if (na_v1 > 0 | na_v2 > 0) { message(var1, " vs. ", var2, ": ", "Omitting missing values. Using ", nrow(x$boot[[1]]) - max(na_v1, na_v2), " observations.") } sdt <- stats::sd(dat_var1 - dat_var2, na.rm = TRUE) t1 <- x %>% dplyr::filter(.data$subgroup == var1) %>% dplyr::select(variable) %>% unlist t2 <- x %>% dplyr::filter(.data$subgroup == var2) %>% dplyr::select(variable) %>% unlist z <- (t1 - t2) / sdt if (t1 - t2 == 0 & is.nan(sdt)) z <- 0 p <- unname(stats::pnorm(-abs(z)) * 2) tibble::tibble(subgroup1 = var1, subgroup2 = var2, test_var = variable, p = p, p_adj = NA, d = t1 - t2, sd_d = sdt, z = z, in_bag = in_bag) }) test_res$p_adj <- unname(stats::p.adjust(p = test_res$p, method = correction)) } else if (!(is.null(y))) { # Test two ungrouped cutpointr objects vs. each other if (!has_boot_results(y)) { stop(paste("y does not contain bootstrap results.", "Was boot_runs > 0 in cutpointr?")) } if (nrow(y) > 1) { stop(paste("When testing two cutpointr objects these objects must not", "contain subgroups.")) } dat_var1 <- x %>% dplyr::select(.data$boot) %>% tidyr::unnest(.data$boot) %>% dplyr::select(paste0(variable, suffix)) %>% unlist dat_var2 <- y %>% dplyr::select(.data$boot) %>% tidyr::unnest(.data$boot) %>% dplyr::select(paste0(variable, suffix)) %>% unlist if (length(dat_var1) != length(dat_var2)) { warning(paste("Unequal number of boot_runs. Using the lower", "number of", min(length(dat_var1), length(dat_var2)), "bootstrap repeats.")) lower_runs <- min(length(dat_var1), length(dat_var2)) dat_var1 <- dat_var1[1:lower_runs] dat_var2 <- dat_var2[1:lower_runs] } na_v1 <- sum(is.na(dat_var1)) na_v2 <- sum(is.na(dat_var2)) if (na_v1 > 0 | na_v2 > 0) { message("Omitting missing values. Using ", nrow(x$boot[[1]]) - max(na_v1, na_v2), " observations.") } sdt <- stats::sd(dat_var1 - dat_var2, na.rm = TRUE) t1 <- x %>% dplyr::select(variable) %>% unlist t2 <- y %>% dplyr::select(variable) %>% unlist z <- (t1 - t2) / sdt if (t1 - t2 == 0 & sdt == 0) z <- 0 p <- stats::pnorm(-abs(z)) * 2 test_res <- tibble::tibble(test_var = variable, p = p, d = t1 - t2, sd_d = sdt, z = z, in_bag = in_bag) } class(test_res) <- c("boot_test", class(test_res)) return(test_res) } cutpointr/R/oc_manual.R0000644000176200001440000000112514726066534014577 0ustar liggesusers#' Set a manual cutpoint for use with cutpointr #' #' This function simply returns \code{cutpoint} as the optimal cutpoint. #' Mainly useful if bootstrap estimates of the out-of-bag performance of a #' given cutpoint are desired, e.g. taking a cutpoint value from the literature. #' #' @inheritParams oc_youden_normal #' @param cutpoint (numeric) The fixed cutpoint. #' @examples #' cutpointr(suicide, dsi, suicide, method = oc_manual, cutpoint = 4) #' @family method functions #' @export oc_manual <- function(cutpoint, ...) { return(data.frame(optimal_cutpoint = cutpoint)) } cutpointr/R/plot_x.R0000644000176200001440000001125114726066534014147 0ustar liggesusers#' Plot the distribution of the independent variable per class from a cutpointr object #' #' Given a \code{cutpointr} object this function plots the distribution(s) of the #' independent variable(s) and the respective cutpoints per class. #' @param x A cutpointr object. #' @param display_cutpoint (logical) Whether or not to display the optimal #' cutpoint as a vertical line. #' @param ... Additional arguments (unused). #' @examples #' opt_cut <- cutpointr(suicide, dsi, suicide) #' plot_x(opt_cut) #' #' ## With subgroup #' opt_cut_2groups <- cutpointr(suicide, dsi, suicide, gender) #' plot_x(opt_cut_2groups) #' @family cutpointr plotting functions #' @export plot_x <- function(x, display_cutpoint = TRUE, ...) { args <- list(...) predictor <- as.character(x$predictor[1]) outcome <- as.character(x$outcome[1]) if (!(has_column(x, "subgroup"))) { res_unnested <- x %>% dplyr::select(.data$data) %>% tidyr::unnest(.data$data) transparency <- 1 if (all(na_inf_omit(unlist(dplyr::select(res_unnested, predictor))) %% 1 == 0) | only_one_unique( na_inf_omit(unlist(dplyr::select(res_unnested, predictor))) )) { all_integer = TRUE dist_plot <- ggplot2::geom_bar(alpha = transparency, position = "identity") } else { all_integer = FALSE dist_plot <- ggplot2::geom_density(alpha = transparency) } dist <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = !!rlang::ensym(predictor))) + dist_plot + # facet by class because always 2 ggplot2::facet_wrap(outcome, scales = "free_y") + ggplot2::ggtitle("Independent variable", "optimal cutpoint and distribution by class") + ggplot2::xlab("value") if (display_cutpoint) { cutpoint_dat <- x %>% dplyr::select(.data$optimal_cutpoint) if (is.list(x$optimal_cutpoint)) { cutpoint_dat <- tidyr::unnest(cols = optimal_cutpoint, data = cutpoint_dat) } dist <- dist + ggplot2::geom_vline(data = cutpoint_dat, mapping = ggplot2::aes(xintercept = optimal_cutpoint), show.legend = FALSE) } } else if (has_column(x, "subgroup")) { res_unnested <- x %>% dplyr::select("data", "subgroup") %>% tidyr::unnest(.data$data) res_unnested <- dplyr::full_join(res_unnested, tibble::as_tibble(x[, c("optimal_cutpoint", "subgroup")]), by = "subgroup") transparency <- 0.6 if (all(na_inf_omit(unlist(dplyr::select(res_unnested, predictor))) %% 1 == 0) | only_one_unique( na_inf_omit(unlist(dplyr::select(res_unnested, predictor))) )) { all_integer = TRUE dist_plot <- ggplot2::geom_bar(alpha = transparency, position = "identity") } else { all_integer = FALSE dist_plot <- ggplot2::geom_density(alpha = transparency) } dist <- ggplot2::ggplot(res_unnested, ggplot2::aes(x = !!rlang::ensym(predictor), fill = subgroup, color = subgroup)) + dist_plot + # facet by class because always 2 ggplot2::facet_wrap(outcome, scales = "free_y") + ggplot2::ggtitle("Independent variable", "optimal cutpoint and distribution by class") + ggplot2::xlab("value") + ggplot2::labs(color = "Subgroup", fill = "Subgroup") if (display_cutpoint) { cutpoint_dat <- x %>% dplyr::select(.data$subgroup, .data$optimal_cutpoint) if (is.list(x$optimal_cutpoint)) { cutpoint_dat <- tidyr::unnest(cols = optimal_cutpoint, data = cutpoint_dat) } dist <- dist + ggplot2::geom_vline(data = cutpoint_dat, mapping = ggplot2::aes(xintercept = optimal_cutpoint, color = subgroup), show.legend = FALSE) } } if (!all_integer) dist <- dist + ggplot2::geom_rug(alpha = 0.5) return(dist) } cutpointr/R/utils.R0000644000176200001440000002661714726066534014016 0ustar liggesusersfind_metric_name <- function(object) { if ("subgroup" %in% colnames(object)) { return(colnames(object)[5]) } else { return(colnames(object)[4]) } } find_metric_name_boot <- function(object) { if ("subgroup" %in% colnames(object)) { return(colnames(object)[6]) } else { return(colnames(object)[5]) } } default_cols <- c("m", "subgroup", "direction", "optimal_cutpoint", "method", "acc", "sensitivity", "specificity", "AUC", "pos_class", "neg_class", "prevalence", "outcome", "predictor", "grouping", "data", "roc_curve", "boot", "tn", "fn", "tp", "fp", "tpr", "tnr", "fpr", "fnr") check_method_cols <- function(method_result) { cn <- colnames(method_result) n_col <- ncol(method_result) identified_cols <- 0 oc_col <- which(cn == "optimal_cutpoint") if (!is.null(oc_col)) identified_cols <- identified_cols + 1 if ("roc_curve" %in% cn) { roc_col <- which(cn == "roc_curve") identified_cols <- identified_cols + 1 } if (identified_cols == 1 & n_col == 2) { metric_col <- (1:n_col)[-oc_col] } else if (identified_cols < n_col) { stopifnot(exists("roc_col")) metric_col <- (1:n_col)[-c(oc_col, roc_col)] if (length(metric_col) >= 2) { stop(paste("method function returned too many columns.", "Should return optimal_cutpoint, roc_curve (optional)", "and a metric column (optional).")) } metric_name <- cn[metric_col] if (metric_name %in% default_cols) { colnames(method_result)[metric_col] <- paste0("metric_", metric_name) } } return(method_result) } check_metric_name <- function(met) { # Numeric vector if (!is.array(met) & is.numeric(met)) return(met) cn <- colnames(met) if (cn %in% default_cols) { colnames(met) <- paste0("metric_", cn) return(met) } else { return(met) } } check_method_name <- function(mod_name) { if (length(mod_name) > 1) { if (mod_name[1] %in% c("::", ":::") & length(mod_name) == 3) { mod_name <- mod_name[3] } else { stop("Could not parse method name") } } return(mod_name) } check_colnames <- function(cutpointr_object) { if ("subgroup" %in% colnames(cutpointr_object)) col_nr <- 4 else col_nr <- 3 metric_name <- colnames(cutpointr_object)[col_nr] if (metric_name %in% default_cols) { metric_name2 <- paste0("metric_", metric_name) colnames(cutpointr_object)[col_nr] <- metric_name2 cutpointr_object$metric_name <- metric_name2 } else { cutpointr_object$metric_name <- metric_name } return(cutpointr_object) } check_roc_curve <- function(object) { if (!("roc_cutpointr" %in% class(object$roc_curve[[1]]))) { stop(paste("roc_curve as returned by the method function is not an", "object of the class roc_cutpointr")) } } has_column <- function(x, colname) { if (colname %in% colnames(x) | colname %in% names(x)) { return(TRUE) } else { return(FALSE) } } has_boot_results <- function(x) { if (has_column(x, "boot")) { if (all(is.na(x[["boot"]]))) { return(FALSE) } else { return(TRUE) } } else { return(FALSE) } } ifel_pos_neg <- function(logi_vec, pos_class, neg_class) { predictions <- rep(neg_class, length(logi_vec)) predictions[logi_vec] <- pos_class return(predictions) } get_fnth <- function(x, n = 1) { x <- unlist(x) if (length(x) == 1) { return(x[1]) } else { return(x[n]) } stop("no conditions apply in get_fnth") } get_numeric_cols <- function(x, class_col) { cols <- colnames(x)[unlist(lapply(x, is.numeric))] cols <- cols[cols != class_col] cols } midpoint <- function(oc, x, direction) { sapply(oc, function(oc) { x <- c(oc, x) if (direction == ">=") { x <- sort(unique(x)) } else { x <- sort(unique(x), decreasing = TRUE) } if (direction == ">=") { mean(c(oc, x[utils::tail(which(x <= oc), 1) - 1])) } else if (direction == "<=") { mean(c(oc, x[utils::tail(which(x >= oc), 1) - 1])) } }) } apply_break_ties <- function(oc, f) { stopifnot(nrow(oc) == 1) optimal_cutpoint <- f(oc[["optimal_cutpoint"]][[1]]) if (length(optimal_cutpoint) > 1) { optimal_cutpoint <- list(optimal_cutpoint) } oc$optimal_cutpoint <- optimal_cutpoint return(oc) } get_opt_ind <- function(roc_curve, oc, direction) { stopifnot(is.numeric(oc) | is.na(oc)) sapply(oc, function(x) { if (direction == ">=") { opt_ind <- max(which(roc_curve$x.sorted >= x)) } else if (direction == "<=") { opt_ind <- max(which(roc_curve$x.sorted <= x)) } return(opt_ind) }) } summary_sd <- function(x) { x <- unlist(x) s <- summary(x)[1:6] result <- c(s[1], stats::quantile(x, 0.05, na.rm = TRUE), s[2:5], stats::quantile(x, 0.95, na.rm = TRUE), s[6], SD = stats::sd(x, na.rm = TRUE), NAs = sum(is.na(x))) return(result) } summary_sd_df <- function(x) { x <- unlist(x) s <- summary(x)[1:6] result <- c(s[1], stats::quantile(x, 0.05, na.rm = TRUE), s[2:5], stats::quantile(x, 0.95, na.rm = TRUE), s[6], SD = stats::sd(x, na.rm = TRUE), NAs = sum(is.na(x))) result <- data.frame(`Min.` = result[1], `5%` = result[2], `1st Qu.` = result[3], `Median` = result[4], `Mean` = result[5], `3rd Qu.` = result[6], `95%` = result[7], `Max.` = result[8], `SD` = result[9], `NAs` = result[10], check.names = FALSE, row.names = NULL) return(result) } # Printing function for data.frames without returning x invisibly and with # row.names = FALSE print_df_nodat <- function (x, ..., digits = NULL, quote = FALSE, right = TRUE, row.names = FALSE, max = NULL) { n <- length(row.names(x)) if (length(x) == 0L) { cat(sprintf(ngettext(n, "data frame with 0 columns and %d row", "data frame with 0 columns and %d rows"), n), "\n", sep = "") } else if (n == 0L) { print.default(names(x), quote = FALSE) cat(gettext("<0 rows> (or 0-length row.names)\n")) } else { if (is.null(max)) max <- getOption("max.print", 99999L) if (!is.finite(max)) stop("invalid 'max' / getOption(\"max.print\"): ", max) omit <- (n0 <- max%/%length(x)) < n m <- as.matrix(format.data.frame(if (omit) x[seq_len(n0), , drop = FALSE] else x, digits = digits, na.encode = FALSE)) if (!isTRUE(row.names)) dimnames(m)[[1L]] <- if (isFALSE(row.names)) rep.int("", if (omit) n0 else n) else row.names print(m, ..., quote = quote, right = right, max = max) if (omit) cat(" [ reached 'max' / getOption(\"max.print\") -- omitted", n - n0, "rows ]\n") } invisible(x) } # If the output of the metric function is no named matrix with one column, # convert it to one. Also run some checks. sanitize_metric <- function(m, m_name, n, silent = TRUE) { if ("data.frame" %in% class(m)) { m <- as.matrix(m) } if (!is.null(dim(m))) { if (dim(m)[2] == 1 & ("matrix" %in% class(m))) { res <- m if (is.null(colnames(res))) colnames(res) <- m_name } else { stop(paste("The metric function should return a numeric vector", "or a one-column matrix or data.frame.")) } } else if (is.numeric(m)) { res <- matrix(m, ncol = 1, dimnames = list(NULL, m_name)) } else { stop(paste("Can't process metric of type", class(m))) } finite_res <- is.finite(res) if (any(!finite_res)) { if (!silent) message("Converting non-finite metric values to NA") res[!finite_res] <- NA } if (nrow(res) != n) { stop("Number of returned metric values not equal to n") } colnames(res) <- make.names(colnames(res)) return(res) } only_one_unique <- function(x) { if (is.character(x) | is.factor(x)) { one_unique_char(x) } else { one_unique_num(x) } } which_cpp <- function(x, y) { if (is.numeric(x)) { return(which_are_num(x, y)) } else { return(which_are_char(x, y)) } } is_equal_cpp <- function(x, y) { if (is.numeric(x)) { return(is_equal_cpp_num(x, y)) } else { return(is_equal_cpp_char(x, as.character(y))) } } na_inf_omit <- function(x) { x <- stats::na.omit(x) x <- x[is.finite(x)] return(x) } .onUnload <- function (libpath) { library.dynam.unload("cutpointr", libpath) } add_list <- function(x, y, name) { if (length(y) > 1) { x[[name]] <- list(y) } else { x[[name]] <- y } return(x) } # rbind list elements that are tibbles with different column types (list or dbl, # may be necessary for bootstrap results, if only one bootstrap resulted in # multiple optimal cutpoints) # Convert non-list columns to list so that bind_rows doesn't complain prepare_bind_rows <- function(x) { stopifnot(inherits(x, "list")) if (length(x) < 2) { return(x) } else { list_cols <- purrr::map(x, function(df) { df %>% purrr::map(function(col) (is.list(col))) }) list_cols <- purrr::map(list_cols, function(coltypes) { which(unlist(coltypes)) }) list_cols <- unique(unlist(list_cols)) x <- purrr::map(x, function(x) { dplyr::mutate_at(.tbl = x, .vars = list_cols, .funs = function(x) { if (!is.list(x)) { purrr::map(x, function(x) x) } else { x } }) }) return(x) } } # Return indices for observations based on nonparametric bootstrap per class. # Preliminary tests suggested that per_class = FALSE leads to better # confidence intervals. simple_boot <- function(ind_pos = NULL, ind_neg = NULL, data = NULL, dep_var = NULL, stratify) { if (stratify) { b_ind_pos <- sample(ind_pos, size = length(ind_pos), replace = TRUE) b_ind_neg <- sample(ind_neg, size = length(ind_neg), replace = TRUE) return(c(b_ind_pos, b_ind_neg)) } else { b_ind <- sample(1:nrow(data), size = nrow(data), replace = TRUE) return(b_ind) } } cutpointr/R/metrics.R0000644000176200001440000007176714726125037014324 0ustar liggesusers sens_spec <- function(tp, fp, tn, fn) { sens <- tp / (tp + fn) spec <- tn / (tn + fp) res <- cbind(sens, spec) colnames(res) <- c("sensitivity", "specificity") return(res) } sesp_from_oc <- function(roc_curve, oc, direction, opt_ind = NULL) { if (is.null(opt_ind)) { opt_ind <- purrr::map_dbl(oc[[1]], function(x) { get_opt_ind(roc_curve = roc_curve, oc = x, direction = direction) }) } sens_spec(tp = roc_curve$tp[opt_ind], fp = roc_curve$fp[opt_ind], tn = roc_curve$tn[opt_ind], fn = roc_curve$fn[opt_ind]) } accuracy_from_oc <- function(roc_curve, oc, direction, opt_ind = NULL) { if (is.null(opt_ind)) { opt_ind <- purrr::map_dbl(oc[[1]], function(x) { get_opt_ind(roc_curve = roc_curve, oc = x, direction = direction) }) opt_ind <- get_opt_ind(roc_curve = roc_curve, oc = oc, direction = direction) } accuracy(tp = roc_curve$tp[opt_ind], fp = roc_curve$fp[opt_ind], tn = roc_curve$tn[opt_ind], fn = roc_curve$fn[opt_ind]) } kappa_from_oc <- function(roc_curve, oc, direction, opt_ind = NULL) { if (is.null(opt_ind)) { opt_ind <- purrr::map_dbl(oc[[1]], function(x) { get_opt_ind(roc_curve = roc_curve, oc = x, direction = direction) }) opt_ind <- get_opt_ind(roc_curve = roc_curve, oc = oc, direction = direction) } cohens_kappa(tp = roc_curve$tp[opt_ind], fp = roc_curve$fp[opt_ind], tn = roc_curve$tn[opt_ind], fn = roc_curve$fn[opt_ind]) } #' Calculate AUC from a roc_cutpointr or cutpointr object #' #' Calculate the area under the ROC curve using the trapezoidal rule. #' #' @param x Data frame resulting from the roc() or cutpointr() function. #' @source Forked from the AUC package #' @return Numeric vector of AUC values #' @name auc #' @export auc <- function(x) { UseMethod("auc", x) } #' @rdname auc #' @export auc.roc_cutpointr <- function(x) { tpr <- x$tpr fpr <- x$fpr l_tpr <- length(tpr) l_fpr <- length(fpr) stopifnot(l_tpr == l_fpr) tpr <- cbind(tpr[2:l_tpr], tpr[1:(l_tpr - 1)]) fpr <- cbind(fpr[2:l_fpr], fpr[1:(l_fpr - 1)]) sum(0.5 * abs(fpr[, 1] - fpr[, 2]) * (tpr[, 1] + tpr[, 2])) } #' @rdname auc #' @export auc.cutpointr <- function(x) { x$AUC } #' Extract the cutpoints from a ROC curve generated by cutpointr #' #' This is a utility function for extracting the cutpoints from a \code{roc_cutpointr} #' object. Mainly useful in conjunction with the \code{plot_cutpointr} function if #' cutpoints are to be plotted on the x-axis. #' #' @name cutpoint #' @param x A roc_cutpointr object. #' @param ... Further arguments. #' @examples #' oc <- cutpointr(suicide, dsi, suicide, gender) #' plot_cutpointr(oc, cutpoint, accuracy) #' #' @family metric functions #' @export cutpoint <- function(x, ...) { x[["x.sorted"]] } #' @rdname cutpoint #' @export cutpoints <- function(x, ...) { x[["x.sorted"]] } #' Calculate accuracy #' #' Calculate accuracy from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' accuracy = (tp + tn) / (tp + fp + tn + fn) #' @param tp (numeric) number of true positives. #' @param fp (numeric) number of false positives. #' @param tn (numeric) number of true negatives. #' @param fn (numeric) number of false negatives. #' @param ... for capturing additional arguments passed by method. #' @examples #' accuracy(10, 5, 20, 10) #' accuracy(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export accuracy <- function(tp, fp, tn, fn, ...) { Accuracy <- cbind((tp + tn) / (tp + fp + tn + fn)) colnames(Accuracy) <- "accuracy" return(Accuracy) } #' Calculate the Youden-Index #' #' Calculate the Youden-Index (J-Index) from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' sensitivity = tp / (tp + fn) \cr #' specificity = tn / (tn + fp) \cr #' youden_index = sensitivity + specificity - 1 \cr #' @inheritParams accuracy #' @examples #' youden(10, 5, 20, 10) #' youden(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export youden <- function(tp, fp, tn, fn, ...) { sesp <- sens_spec(tp, fp, tn, fn) youden <- cbind(rowSums(sesp) - 1) colnames(youden) <- "youden" return(youden) } #' Calculate sensitivity #' #' Calculate sensitivity from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' sensitivity = tp / (tp + fn) \cr #' @inheritParams accuracy #' @examples #' sensitivity(10, 5, 20, 10) #' sensitivity(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export sensitivity <- function(tp, fn, ...) { sens <- tp / (tp + fn) sens <- matrix(sens, ncol = 1) colnames(sens) <- "sensitivity" return(sens) } #' Calculate specificity #' #' Calculate specificity from true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' specificity = tn / (tn + fp) \cr #' @inheritParams accuracy #' @examples #' specificity(10, 5, 20, 10) #' specificity(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export specificity <- function(fp, tn, ...) { spec <- tn / (tn + fp) spec <- matrix(spec, ncol = 1) colnames(spec) <- "specificity" return(spec) } #' Metrics that are constrained by another metric #' #' For example, calculate sensitivity where #' a lower bound (minimal desired value) for specificty can be defined. All returned #' metric values for cutpoints that lead to values of the constraining metric #' below the specified minimum will be zero. #' The inputs must be vectors of equal length. #' @examples #' ## Maximum sensitivity when Positive Predictive Value (PPV) is at least 75% #' library(dplyr) #' library(purrr) #' library(cutpointr) #' cp <- cutpointr(data = suicide, x = dsi, class = suicide, #' method = maximize_metric, #' metric = sens_constrain, #' constrain_metric = ppv, #' min_constrain = 0.75) #' ## All metric values (m) where PPV < 0.75 are zero #' plot_metric(cp) #' cp$roc_curve #' ## We can confirm that PPV is indeed >= 0.75 #' cp %>% #' add_metric(list(ppv)) #' ## We can also do so for the complete ROC curve(s) #' cp %>% #' pull(roc_curve) %>% #' map(~ add_metric(., list(sensitivity, ppv))) #' #' ## Use the metric_constrain function for a combination of any two metrics #' ## Estimate optimal cutpoint for precision given a recall of at least 70% #' cp <- cutpointr(data = suicide, x = dsi, class = suicide, #' subgroup = gender, #' method = maximize_metric, #' metric = metric_constrain, #' main_metric = precision, #' suffix = "_constrained", #' constrain_metric = recall, #' min_constrain = 0.70) #' ## All metric values (m) where recall < 0.7 are zero #' plot_metric(cp) #' ## We can confirm that recall is indeed >= 0.70 and that precision_constrain #' ## is identical to precision for the estimated cutpoint #' cp %>% #' add_metric(list(recall, precision)) #' ## We can also do so for the complete ROC curve(s) #' cp %>% #' pull(roc_curve) %>% #' map(~ add_metric(., list(recall, precision))) #' @inheritParams accuracy #' @family metric functions #' @param constrain_metric Metric for constraint. #' @param min_constrain Minimum desired value of constrain_metric. #' @param main_metric Metric to be optimized. #' @param suffix Character string to be added to the name of main_metric. #' @name metric_constrain #' @export metric_constrain <- function(tp, fp, tn, fn, main_metric = sensitivity, constrain_metric = specificity, min_constrain = 0.5, suffix = "_constrain", ...) { metric_constrain <- main_metric(tp = tp, fp = fp, tn = tn, fn = fn) metric_constrain <- sanitize_metric(m = metric_constrain, n = nrow(metric_constrain), m_name = "metric_constrain") constraint <- constrain_metric(tp = tp, fp = fp, tn = tn, fn = fn) constraint <- as.numeric(unlist(constraint)) failed_constr <- constraint < min_constrain metric_constrain[failed_constr, ] <- 0 if (all(failed_constr)) { warning("No cutpoint was found that satisfies the constraint.") } colnames(metric_constrain) <- paste0(colnames(metric_constrain), suffix) return(metric_constrain) } #' @rdname metric_constrain #' @export sens_constrain <- function(tp, fp, tn, fn, constrain_metric = specificity, min_constrain = 0.5, ...) { metric_constrain <- sensitivity(tp = tp, fn = fn) constraint <- constrain_metric(tp = tp, fp = fp, tn = tn, fn = fn) constraint <- as.numeric(unlist(constraint)) failed_constr <- constraint < min_constrain if (all(failed_constr)) { warning("No cutpoint was found that satisfies the constraint.") } metric_constrain[failed_constr, ] <- 0 colnames(metric_constrain) <- "sens_constrain" return(metric_constrain) } #' @rdname metric_constrain #' @export spec_constrain <- function(tp, fp, tn, fn, constrain_metric = sensitivity, min_constrain = 0.5, ...) { metric_constrain <- specificity(fp = fp, tn = tn) constraint <- constrain_metric(tp = tp, fp = fp, tn = tn, fn = fn) constraint <- as.numeric(unlist(constraint)) failed_constr <- constraint < min_constrain if (all(failed_constr)) { warning("No cutpoint was found that satisfies the constraint.") } metric_constrain[failed_constr, ] <- 0 colnames(metric_constrain) <- "spec_constrain" return(metric_constrain) } #' @rdname metric_constrain #' @export acc_constrain <- function(tp, fp, tn, fn, constrain_metric = sensitivity, min_constrain = 0.5, ...) { metric_constrain <- accuracy(tp = tp, tn = tn, fp = fp, fn = fn) constraint <- constrain_metric(tp = tp, fp = fp, tn = tn, fn = fn) constraint <- as.numeric(unlist(constraint)) failed_constr <- constraint < min_constrain if (all(failed_constr)) { warning("No cutpoint was found that satisfies the constraint.") } metric_constrain[failed_constr, ] <- 0 colnames(metric_constrain) <- "acc_constrain" return(metric_constrain) } #' Calculate the sum of sensitivity and specificity #' #' Calculate the sum of sensitivity and specificity from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' sensitivity = tp / (tp + fn) \cr #' specificity = tn / (tn + fp) \cr #' sum_sens_spec = sensitivity + specificity \cr #' @inheritParams accuracy #' @examples #' sum_sens_spec(10, 5, 20, 10) #' sum_sens_spec(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export sum_sens_spec <- function(tp, fp, tn, fn, ...) { sesp <- sens_spec(tp, fp, tn, fn) sesp <- cbind(rowSums(sesp)) colnames(sesp) <- "sum_sens_spec" return(sesp) } #' Calculate the product of sensitivity and specificity #' #' Calculate the product of sensitivity and specificity from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' sensitivity = tp / (tp + fn) \cr #' specificity = tn / (tn + fp) \cr #' prod_sens_spec = sensitivity * specificity \cr #' @inheritParams accuracy #' @examples #' prod_sens_spec(10, 5, 20, 10) #' prod_sens_spec(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export prod_sens_spec <- function(tp, fp, tn, fn, ...) { sesp <- sens_spec(tp, fp, tn, fn) sesp <- cbind(sesp[, 1] * sesp[, 2]) colnames(sesp) <- "prod_sens_spec" return(sesp) } #' Calculate the absolute difference of sensitivity and specificity #' #' Calculate the absolute difference of sensitivity and specificity #' from true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr #' \cr #' sensitivity = tp / (tp + fn) \cr #' specificity = tn / (tn + fp) \cr #' abs_d_sens_spec = |sensitivity - specificity| \cr #' @inheritParams accuracy #' @examples #' abs_d_sens_spec(10, 5, 20, 10) #' abs_d_sens_spec(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export abs_d_sens_spec <- function(tp, fp, tn, fn, ...) { sesp <- sens_spec(tp, fp, tn, fn) abs_d_sesp <- abs(sesp[, 1] - sesp[, 2]) abs_d_sesp <- matrix(abs_d_sesp, ncol = 1) colnames(abs_d_sesp) <- "abs_d_sens_spec" return(abs_d_sesp) } #' Calculate the distance between points on the ROC curve and (0,1) #' #' Calculate the distance on the ROC space between points on the ROC curve #' and the point of perfect discrimination #' from true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. To be used with #' \code{method = minimize_metric}. \cr #' \cr #' sensitivity = tp / (tp + fn) \cr #' specificity = tn / (tn + fp) \cr #' roc01 = sqrt((1 - sensitivity)^2 + (1 - specificity)^2) \cr #' @inheritParams accuracy #' @examples #' roc01(10, 5, 20, 10) #' roc01(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' oc <- cutpointr(suicide, dsi, suicide, #' method = minimize_metric, metric = roc01) #' plot_roc(oc) #' @family metric functions #' @export roc01 <- function(tp, fp, tn, fn, ...) { sesp <- sens_spec(tp, fp, tn, fn) distance <- sqrt((1 - sesp[, 1])^2 + (1 - sesp[, 2])^2) distance <- matrix(distance, ncol = 1) colnames(distance) <- "roc01" return(distance) } #' Calculate precision #' #' Calculate precision (equal to the positive predictive value) #' from true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' precision = tp / (tp + fp) \cr #' @inheritParams accuracy #' @examples #' precision(10, 5, 20, 10) #' precision(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export precision <- function(tp, fp, tn, fn, ...) { prec <- ppv(tp = tp, fp = fp, tn = tn, fn = fn) colnames(prec) <- "precision" return(prec) } #' Calculate recall #' #' Calculate recall (equal to sensitivity) from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' recall = tp / (tp + fn) \cr #' @inheritParams accuracy #' @examples #' recall(10, 5, 20, 10) #' recall(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export recall <- function(tp, fp, tn, fn, ...) { rec <- sensitivity(tp = tp, fp = fp, tn = tn, fn = fn) colnames(rec) <- "recall" return(rec) } #' Calculate the positive predictive value #' #' Calculate the positive predictive value (PPV) from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' ppv = tp / (tp + fp) \cr #' @inheritParams accuracy #' @examples #' ppv(10, 5, 20, 10) #' ppv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export ppv <- function(tp, fp, tn, fn, ...) { ppv <- tp / (tp + fp) ppv <- matrix(ppv, ncol = 1) colnames(ppv) <- "ppv" return(ppv) } #' Calculate the negative predictive value #' #' Calculate the negative predictive value (NPV) #' from true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' npv = tn / (tn + fn) \cr #' @inheritParams accuracy #' @examples #' npv(10, 5, 20, 10) #' npv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export npv <- function(tp, fp, tn, fn, ...) { npv <- tn / (tn + fn) npv <- matrix(npv, ncol = 1) colnames(npv) <- "npv" return(npv) } #' Calculate the absolute difference of positive and negative predictive value #' #' Calculate the absolute difference of positive predictive value (PPV) and #' negative predictive value (NPV) from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' ppv = tp / (tp + fp) \cr #' npv = tn / (tn + fn) \cr #' @inheritParams accuracy #' @examples #' abs_d_ppv_npv(10, 5, 20, 10) #' abs_d_ppv_npv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export abs_d_ppv_npv <- function(tp, fp, tn, fn, ...) { ppv <- tp / (tp + fp) npv <- tn / (tn + fn) abs_d_ppvnpv <- abs(ppv - npv) abs_d_ppvnpv <- matrix(abs_d_ppvnpv, ncol = 1) colnames(abs_d_ppvnpv) <- "abs_d_ppv_npv" return(abs_d_ppvnpv) } #' Calculate the sum of positive and negative predictive value #' #' Calculate the sum of positive predictive value (PPV) and #' negative predictive value (NPV) from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' ppv = tp / (tp + fp) \cr #' npv = tn / (tn + fn) \cr #' sum_ppv_npv = ppv + npv \cr #' @inheritParams accuracy #' @examples #' sum_ppv_npv(10, 5, 20, 10) #' sum_ppv_npv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export sum_ppv_npv <- function(tp, fp, tn, fn, ...) { ppv <- tp / (tp + fp) npv <- tn / (tn + fn) sum_ppvnpv <- ppv + npv sum_ppvnpv <- matrix(sum_ppvnpv, ncol = 1) colnames(sum_ppvnpv) <- "sum_ppv_npv" return(sum_ppvnpv) } #' Calculate the product of positive and negative predictive value #' #' Calculate the product of positive predictive value (PPV) and #' negative predictive value (NPV) from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' ppv = tp / (tp + fp) \cr #' npv = tn / (tn + fn) \cr #' prod_ppv_npv = ppv * npv \cr #' @inheritParams accuracy #' @examples #' prod_ppv_npv(10, 5, 20, 10) #' prod_ppv_npv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export prod_ppv_npv <- function(tp, fp, tn, fn, ...) { ppv <- tp / (tp + fp) npv <- tn / (tn + fn) prod_ppvnpv <- ppv * npv prod_ppvnpv <- matrix(prod_ppvnpv, ncol = 1) colnames(prod_ppvnpv) <- "prod_ppv_npv" return(prod_ppvnpv) } #' Calculate the false omission and false discovery rate #' #' Calculate the false omission rate or false discovery rate #' from true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' false_omission_rate = fn / (tn + fn) = 1 - npv #' false_discovery_rate = fp / (tp + fp) = 1 - ppv #' @inheritParams accuracy #' @examples #' false_omission_rate(10, 5, 20, 10) #' false_omission_rate(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @name false_omission_rate #' @family metric functions #' @export false_omission_rate <- function(tp, fp, tn, fn, ...) { fomr <- fn / (tn + fn) fomr <- matrix(fomr, ncol = 1) colnames(fomr) <- "false_omission_rate" return(fomr) } #' @rdname false_omission_rate #' @export false_discovery_rate <- function(tp, fp, tn, fn, ...) { fdr <- fp / (tp + fp) fdr <- matrix(fdr, ncol = 1) colnames(fdr) <- "false_discovery_rate" return(fdr) } #' Calculate true / false positive / negative rate #' #' Calculate the true positive rate (tpr, equal to sensitivity and recall), #' the false positive rate (fpr, equal to fall-out), #' the true negative rate (tnr, equal to specificity), #' or the false negative rate (fnr) from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' tpr = tp / (tp + fn) \cr #' fpr = fp / (fp + tn) \cr #' tnr = tn / (tn + fp) \cr #' fnr = fn / (fn + tp) \cr #' @inheritParams accuracy #' @name tpr #' @examples #' tpr(10, 5, 20, 10) #' tpr(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export tpr <- function(tp, fn, ...) { tprate <- sensitivity(tp = tp, fn = fn) colnames(tprate) <- "tpr" return(tprate) } #' @rdname tpr #' @export fpr <- function(fp, tn, ...) { fprate <- matrix(fp / (fp + tn), ncol = 1) colnames(fprate) <- "fpr" return(fprate) } #' @rdname tpr #' @export tnr <- function(fp, tn, ...) { tnrate <- specificity(tn = tn, fp = fp) colnames(tnrate) <- "tnr" return(tnrate) } #' @rdname tpr #' @export fnr <- function(tp, fn, ...) { fnrate <- matrix(fn / (fn + tp), ncol = 1) colnames(fnrate) <- "fnr" return(fnrate) } #' Calculate the positive or negative likelihood ratio #' #' Calculate the positive or negative likelihood ratio #' from true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' plr = tpr / fpr \cr #' nlr = fnr / tnr \cr #' @name plr #' @inheritParams accuracy #' @examples #' plr(10, 5, 20, 10) #' plr(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export plr <- function(tp, fp, tn, fn, ...) { plr <- tpr(tp = tp, fp = tp, tn = tn, fn = fn) / fpr(tp = tp, fp = fp, tn = tn, fn = fn) plr <- matrix(plr, ncol = 1) colnames(plr) <- "plr" return(plr) } #' @rdname plr #' @export nlr <- function(tp, fp, tn, fn, ...) { nlr <- fnr(tp = tp, fp = tp, tn = tn, fn = fn) / tnr(tp = tp, fp = fp, tn = tn, fn = fn) nlr <- matrix(nlr, ncol = 1) colnames(nlr) <- "nlr" return(nlr) } #' Extract number true / false positives / negatives #' #' Extract the number of true positives (tp), false positives (fp), #' true negatives (tn), or false negatives (fn). #' The inputs must be vectors of equal length. Mainly useful for \code{plot_cutpointr}. #' @name tp #' @inheritParams accuracy #' @examples #' tp(10, 5, 20, 10) #' tp(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' fp(10, 5, 20, 10) #' tn(10, 5, 20, 10) #' fn(10, 5, 20, 10) #' @family metric functions #' @export tp <- function(tp, ...) { res <- matrix(tp, ncol = 1) colnames(res) <- "tp" return(res) } #' @rdname tp #' @export tn <- function(tn, ...) { res <- matrix(tn, ncol = 1) colnames(res) <- "tn" return(res) } #' @rdname tp #' @export fp <- function(fp, ...) { res <- matrix(fp, ncol = 1) colnames(res) <- "fp" return(res) } #' @rdname tp #' @export fn <- function(fn, ...) { res <- matrix(fn, ncol = 1) colnames(res) <- "fn" return(res) } #' Calculate Cohen's Kappa #' #' Calculate the Kappa metric from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' mrg_a = ((tp + fn) * (tp + fp)) / (tp + fn + fp + tn) \cr #' mrg_b = ((fp + tn) * (fn + tn)) / (tp + fn + fp + tn) \cr #' expec_agree = (mrg_a + mrg_b) / (tp + fn + fp + tn) \cr #' obs_agree = (tp + tn) / (tp + fn + fp + tn) \cr #' cohens_kappa = (obs_agree - expec_agree) / (1 - expec_agree) \cr #' @inheritParams accuracy #' @examples #' cohens_kappa(10, 5, 20, 10) #' cohens_kappa(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @return A numeric matrix with the column name "cohens_kappa". #' @family metric functions #' @export cohens_kappa <- function(tp, fp, tn, fn, ...) { mrg_a <- ((tp + fn) * (tp + fp)) / (tp + fn + fp + tn) mrg_b <- ((fp + tn) * (fn + tn)) / (tp + fn + fp + tn) EA <- (mrg_a + mrg_b) / (tp + fn + fp + tn) OA <- (tp + tn) / (tp + fn + fp + tn) res <- matrix((OA - EA) / (1 - EA), ncol = 1) colnames(res) <- "cohens_kappa" return(res) } #' Calculate the odds ratio #' #' Calculate the (diagnostic) odds ratio from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' odds_ratio = (tp / fp) / (fn / tn) \cr #' @inheritParams accuracy #' @examples #' odds_ratio(10, 5, 20, 10) #' odds_ratio(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export odds_ratio <- function(tp, fp, tn, fn, ...) { or <- (tp / fp) / (fn / tn) or <- matrix(or, ncol = 1) colnames(or) <- "odds_ratio" return(or) } #' Calculate the risk ratio (relative risk) #' #' Calculate the risk ratio (or relative risk) from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' risk_ratio = (tp / (tp + fn)) / (fp / (fp + tn)) \cr #' @inheritParams accuracy #' @examples #' risk_ratio(10, 5, 20, 10) #' risk_ratio(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export risk_ratio <- function(tp, fp, tn, fn, ...) { rr <- (tp / (tp + fn)) / (fp / (fp + tn)) rr <- matrix(rr, ncol = 1) colnames(rr) <- "risk_ratio" return(rr) } #' Calculate the p-value of a chi-squared test #' #' Calculate the p-value of a chi-squared test from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. #' @inheritParams accuracy #' @examples #' p_chisquared(10, 5, 20, 10) #' p_chisquared(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export p_chisquared <- function(tp, fp, tn, fn, ...) { samplesize <- tp + fp + tn + fn chisq <- (samplesize * ((tp * tn - fp * fn) ** 2)) / ((tp + fp) * (fn + tn) * (tp + fn) * (fp + tn)) pval <- stats::pchisq(chisq, 1, lower.tail = F) pval <- matrix(pval, ncol = 1) colnames(pval) <- "p_chisquared" return(pval) } #' Calculate the misclassification cost #' #' Calculate the misclassification cost from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' misclassification_cost = cost_fp * fp + cost_fn * fn \cr #' @inheritParams accuracy #' @param cost_fp (numeric) the cost of a false positive #' @param cost_fn (numeric) the cost of a false negative #' @examples #' misclassification_cost(10, 5, 20, 10, cost_fp = 1, cost_fn = 5) #' misclassification_cost(c(10, 8), c(5, 7), c(20, 12), c(10, 18), #' cost_fp = 1, cost_fn = 5) #' @family metric functions #' @export misclassification_cost <- function(tp, fp, tn, fn, cost_fp = 1, cost_fn = 1, ...) { misclassification_cost <- cost_fp * fp + cost_fn * fn misclassification_cost <- matrix(misclassification_cost, ncol = 1) colnames(misclassification_cost) <- "misclassification_cost" return(misclassification_cost) } #' Calculate the total utility #' #' Calculate the total utility from #' true positives, false positives, true negatives and false negatives. \cr \cr #' total_utility = utility_tp * tp + utility_tn * tn - cost_fp * fp - cost_fn * fn \cr \cr #' The inputs must be vectors of equal length. #' @inheritParams accuracy #' @param utility_tp (numeric) the utility of a true positive #' @param utility_tn (numeric) the utility of a true negative #' @param cost_fp (numeric) the cost of a false positive #' @param cost_fn (numeric) the cost of a false negative #' @examples #' total_utility(10, 5, 20, 10, utility_tp = 3, utility_tn = 3, cost_fp = 1, cost_fn = 5) #' total_utility(c(10, 8), c(5, 7), c(20, 12), c(10, 18), #' utility_tp = 3, utility_tn = 3, cost_fp = 1, cost_fn = 5) #' @family metric functions #' @export total_utility <- function(tp, fp, tn, fn, utility_tp = 1, utility_tn = 1, cost_fp = 1, cost_fn = 1, ...) { utility <- utility_tp * tp + utility_tn * tn - cost_fp * fp - cost_fn * fn utility <- matrix(utility, ncol = 1) colnames(utility) <- "total_utility" return(utility) } #' Calculate the F1-score #' #' Calculate the F1-score from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' F1_score = (2 * tp) / (2 * tp + fp + fn) \cr #' @inheritParams accuracy #' @examples #' F1_score(10, 5, 20, 10) #' F1_score(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export F1_score <- function(tp, fp, tn, fn, ...) { f <- (2 * tp) / (2 * tp + fp + fn) f <- matrix(f, ncol = 1) colnames(f) <- "F1_score" return(f) } #' Calculate the Jaccard Index #' #' Calculate the Jaccard Index from #' true positives, false positives, true negatives and false negatives. #' The inputs must be vectors of equal length. \cr \cr #' Jaccard = (tp) / (tp + fp + fn) \cr #' @inheritParams accuracy #' @examples #' Jaccard(10, 5, 20, 10) #' Jaccard(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) #' @family metric functions #' @export Jaccard <- function(tp, fp, tn, fn, ...) { f <- tp / (tp + fp + fn) f <- matrix(f, ncol = 1) colnames(f) <- "Jaccard" return(f) } cutpointr/R/boot_ci.R0000644000176200001440000000711114726066534014260 0ustar liggesusers#' Calculate bootstrap confidence intervals from a cutpointr object #' #' Given a \code{cutpointr} object that includes bootstrap results #' this function calculates a bootstrap #' confidence interval for a selected variable. #' Missing values are removed before calculating the quantiles. In the case #' of multiple optimal cutpoints all cutpoints / metric values are included #' in the calculation. #' Values of the selected variable are returned for the percentiles alpha / 2 #' and 1 - alpha / 2. The metrics in the bootstrap data frames of #' \code{cutpointr} are suffixed with \code{_b} and \code{_oob} to indicate #' in-bag and out-of-bag, respectively. For example, to calculate quantiles #' of the in-bag AUC \code{variable = AUC_b} should be set. #' #' @param x A cutpointr object with bootstrap results #' @param variable Variable to calculate CI for #' @param alpha Alpha level. Quantiles of the bootstrapped values are returned #' for (alpha / 2) and 1 - (alpha / 2). #' @param in_bag Whether the in-bag or out-of-bag results should be used for testing #' @return A data frame with the columns quantile and value #' @examples #' \dontrun{ #' opt_cut <- cutpointr(suicide, dsi, suicide, gender, #' metric = youden, boot_runs = 1000) #' boot_ci(opt_cut, optimal_cutpoint, in_bag = FALSE, alpha = 0.05) #' boot_ci(opt_cut, acc, in_bag = FALSE, alpha = 0.05) #' boot_ci(opt_cut, cohens_kappa, in_bag = FALSE, alpha = 0.05) #' boot_ci(opt_cut, AUC, in_bag = TRUE, alpha = 0.05) #' } #' @export #' @family main cutpointr functions boot_ci <- function(x, variable, in_bag = TRUE, alpha = 0.05) { if (alpha < 0 | alpha > 1) stop("alpha should be between 0 and 1.") if (!inherits(x, "cutpointr")) { stop("Only objects of type cutpointr are supported") } if (!has_boot_results(x)) { stop("No bootstrap results found. Was boot_runs > 0 in cutpointr?") } if (!("subgroup" %in% colnames(x))) { variable <- rlang::enquo(variable) variable <- rlang::as_name(variable) if (in_bag) suffix <- "_b" else suffix <- "_oob" if (variable == "optimal_cutpoint") { suffix <- "" in_bag = TRUE } variable <- paste0(variable, suffix) variable <- x %>% dplyr::select(.data$boot) %>% tidyr::unnest(cols = .data$boot) %>% dplyr::pull(variable) %>% unlist() values <- stats::quantile(variable, probs = c(alpha / 2, 1 - alpha / 2), na.rm = TRUE) res <- tibble::tibble( quantile = c(alpha / 2, 1 - alpha / 2), values = unname(values) ) return(res) } else { res <- purrr::map2_dfr(.x = x$subgroup, .y = x$boot, .f = function(s, b) { variable <- rlang::enquo(variable) variable <- rlang::as_name(variable) if (in_bag) suffix <- "_b" else suffix <- "_oob" if (variable == "optimal_cutpoint") { suffix <- "" in_bag = TRUE } variable <- paste0(variable, suffix) variable <- b %>% dplyr::pull(variable) %>% unlist() values <- stats::quantile(variable, probs = c(alpha / 2, 1 - alpha / 2), na.rm = TRUE) tibble::tibble( subgroup = s, quantile = c(alpha / 2, 1 - alpha / 2), values = unname(values) ) }) return(res) } } cutpointr/R/plot_cut_boot.R0000644000176200001440000000510214726066534015514 0ustar liggesusers#' Plot the bootstrapped distribution of optimal cutpoints from a cutpointr object #' #' Given a cutpointr object this function plots the bootstrapped distribution #' of optimal cutpoints. \code{cutpointr} has to be run with \code{boot_runs}` > 0 #' to enable bootstrapping. #' @param x A cutpointr object. #' @param ... Additional arguments (unused). #' @examples #' set.seed(100) #' opt_cut <- cutpointr(suicide, dsi, suicide, boot_runs = 10) #' plot_cut_boot(opt_cut) #' @family cutpointr plotting functions #' @export plot_cut_boot <- function(x, ...) { if(!("cutpointr" %in% class(x))) stop("x is no cutpointr object.") args <- list(...) if (!(has_column(x, "subgroup"))) { dts_boot <- "boot" dts <- "data" fll <- NULL clr <- NULL transparency <- 1 } else { dts_boot <- c("boot", "subgroup") dts <- c("data", "subgroup") fll <- "subgroup" clr <- "subgroup" transparency <- 0.6 } if (has_boot_results(x)) { res_boot_unnested <- x %>% dplyr::select(dts_boot) %>% dplyr::mutate(boot = prepare_bind_rows(.data$boot)) %>% tidyr::unnest(.data$boot) cutpoints <- unlist(res_boot_unnested$optimal_cutpoint) if (all(na_inf_omit(cutpoints %% 1 == 0)) | only_one_unique(na_inf_omit(cutpoints))) { all_integer = TRUE dist_plot <- ggplot2::geom_bar(alpha = transparency, position = "identity") } else { all_integer = FALSE dist_plot <- ggplot2::geom_density(alpha = transparency) } # If multiple optimal cutpoints optimal_cutpoint is a list if (is.list(res_boot_unnested$optimal_cutpoint)) { res_boot_unnested <- res_boot_unnested %>% dplyr::select(-c("roc_curve_b", "roc_curve_oob")) %>% tidyr::unnest() } boot_cut <- suppressMessages( ggplot2::ggplot(res_boot_unnested, ggplot2::aes_string(x = "optimal_cutpoint", fill = fll, color = clr)) + dist_plot + ggplot2::ggtitle("Bootstrap", "distribution of optimal cutpoints") + ggplot2::xlab("optimal cutpoint") ) if (!all_integer) boot_cut <- boot_cut + ggplot2::geom_rug(alpha = 0.5) } else { stop("No bootstrap results found. Was boot_runs > 0 in cutpointr?") } return(boot_cut) } cutpointr/R/compat-lifecycle.R0000644000176200001440000002002014726066534016054 0ustar liggesusers# nocov start --- compat-lifecycle --- 2019-06-13 Wed 10:58 # This file serves as a reference for currently unexported rlang # lifecycle functions. Please find the most recent version in rlang's # repository. These functions require rlang in your `Imports` # DESCRIPTION field but you don't need to import rlang in your # namespace. #' Signal deprecation #' #' @description #' #' These functions provide two levels of verbosity for deprecation #' warnings. #' #' * `signal_soft_deprecated()` warns only if called from the global #' environment (so the user can change their script) or from the #' package currently being tested (so the package developer can fix #' the package). #' #' * `warn_deprecated()` warns unconditionally. #' #' * `stop_defunct()` fails unconditionally. #' #' Both functions warn only once per session by default to avoid #' overwhelming the user with repeated warnings. #' #' @param msg The deprecation message. #' @param id The id of the deprecation. A warning is issued only once #' for each `id`. Defaults to `msg`, but you should give a unique ID #' when the message is built programmatically and depends on inputs. #' @param env The environment in which the soft-deprecated function #' was called. A warning is issued if called from the global #' environment. If testthat is running, a warning is also called if #' the retired function was called from the package being tested. #' #' @section Controlling verbosity: #' #' The verbosity of retirement warnings can be controlled with global #' options. You'll generally want to set these options locally with #' one of these helpers: #' #' * `with_lifecycle_silence()` disables all soft-deprecation and #' deprecation warnings. #' #' * `with_lifecycle_warnings()` enforces warnings for both #' soft-deprecated and deprecated functions. The warnings are #' repeated rather than signalled once per session. #' #' * `with_lifecycle_errors()` enforces errors for both #' soft-deprecated and deprecated functions. #' #' All the `with_` helpers have `scoped_` variants that are #' particularly useful in testthat blocks. #' #' @noRd #' @seealso [lifecycle()] NULL signal_soft_deprecated <- function(msg, id = msg, env = rlang::caller_env(2)) { msg <- lifecycle_validate_message(msg) stopifnot( rlang::is_string(id), rlang::is_environment(env) ) if (rlang::is_true(rlang::peek_option("lifecycle_disable_warnings"))) { return(invisible(NULL)) } env_inherits_global <- function(env) { # `topenv(emptyenv())` returns the global env. Return `FALSE` in # that case to allow passing the empty env when the # soft-deprecation should not be promoted to deprecation based on # the caller environment. if (rlang::is_reference(env, emptyenv())) { return(FALSE) } rlang::is_reference(topenv(env), rlang::global_env()) } if (rlang::is_true(rlang::peek_option("lifecycle_verbose_soft_deprecation")) || env_inherits_global(env)) { warn_deprecated(msg, id) return(invisible(NULL)) } # Test for environment names rather than reference/contents because # testthat clones the namespace tested_package <- Sys.getenv("TESTTHAT_PKG") if (nzchar(tested_package) && identical(Sys.getenv("NOT_CRAN"), "true") && rlang::env_name(topenv(env)) == rlang::env_name(rlang::ns_env(tested_package))) { warn_deprecated(msg, id) return(invisible(NULL)) } rlang::signal(msg, "lifecycle_soft_deprecated") } warn_deprecated <- function(msg, id = msg) { msg <- lifecycle_validate_message(msg) stopifnot(rlang::is_string(id)) if (rlang::is_true(rlang::peek_option("lifecycle_disable_warnings"))) { return(invisible(NULL)) } if (!rlang::is_true(rlang::peek_option("lifecycle_repeat_warnings")) && rlang::env_has(deprecation_env, id)) { return(invisible(NULL)) } rlang::env_poke(deprecation_env, id, TRUE); has_colour <- function() rlang::is_installed("crayon") && crayon::has_color() silv <- function(x) if (has_colour()) crayon::silver(x) else x if (rlang::is_true(rlang::peek_option("lifecycle_warnings_as_errors"))) { .Signal <- stop_defunct } else { .Signal <- .Deprecated } if (!rlang::is_true(rlang::peek_option("lifecycle_repeat_warnings"))) { msg <- paste0(msg, "\n", silv("This warning is displayed once per session.")) } .Signal(msg = msg) } deprecation_env <- new.env(parent = emptyenv()) stop_defunct <- function(msg) { msg <- lifecycle_validate_message(msg) err <- rlang::cnd( c("defunctError", "error", "condition"), old = NULL, new = NULL, package = NULL, message = msg ) stop(err) } scoped_lifecycle_silence <- function(frame = rlang::caller_env()) { rlang::scoped_options(.frame = frame, lifecycle_disable_warnings = TRUE ) } with_lifecycle_silence <- function(expr) { scoped_lifecycle_silence() expr } scoped_lifecycle_warnings <- function(frame = rlang::caller_env()) { rlang::scoped_options(.frame = frame, lifecycle_disable_warnings = FALSE, lifecycle_verbose_soft_deprecation = TRUE, lifecycle_repeat_warnings = TRUE ) } with_lifecycle_warnings <- function(expr) { scoped_lifecycle_warnings() expr } scoped_lifecycle_errors <- function(frame = rlang::caller_env()) { scoped_lifecycle_warnings(frame = frame) rlang::scoped_options(.frame = frame, lifecycle_warnings_as_errors = TRUE ) } with_lifecycle_errors <- function(expr) { scoped_lifecycle_errors() expr } #' Embed a lifecycle badge in documentation #' #' @description #' #' Use `lifecycle()` within a `Sexpr` macro to embed a #' [lifecycle](https://www.tidyverse.org/lifecycle/) badge in your #' documentation. The badge should appear first in the description: #' #' ``` #' \Sexpr[results=rd, stage=render]{mypkg:::lifecycle("questioning")} #' ``` #' #' The badge appears as an image in the HTML version of the #' documentation. To make them available in your package, visit #' and copy #' all the files starting with `lifecycle-` in your `man/figures/` #' folder. #' #' @param stage A lifecycle stage as a string, one of: #' `"experimental"`, `"maturing"`, `"stable"`, `"questioning"`, #' `"archived"`, `"soft-deprecated"`, `"deprecated"`, `"defunct"`. #' #' @noRd NULL upcase1 <- function(x) { substr(x, 1, 1) <- toupper(substr(x, 1, 1)) x } lifecycle_img <- function(stage, url) { file <- sprintf("lifecycle-%s.svg", stage) stage_alt <- upcase1(stage) switch(stage, experimental = , maturing = , stable = , questioning = , retired = , archived = sprintf( "\\out{%s lifecycle}", url, file.path("figures", file), stage_alt ) , `soft-deprecated` = , deprecated = , defunct = sprintf( "\\figure{%s}{options: alt='%s lifecycle'}", file, stage_alt ), rlang::abort(sprintf("Unknown lifecycle stage `%s`", stage)) ) } lifecycle_validate_message <- function(msg) { stopifnot(rlang::is_character(msg)) paste0(msg, collapse = "\n") } lifecycle <- function(stage) { url <- paste0("https://www.tidyverse.org/lifecycle/#", stage) img <- lifecycle_img(stage, url) sprintf( "\\ifelse{html}{%s}{\\strong{%s}}", img, upcase1(stage) ) }cutpointr/R/data.R0000644000176200001440000000376614726066534013567 0ustar liggesusers#' Suicide attempts and DSI sum scores of 532 subjects #' #' Various personality and clinical psychological characteristics were assessed #' as part of an online-study preventing suicide. To identify persons at risk #' for attempting suicide, various demographic and clinical characteristics #' were assessed. Depressive Symptom Inventory - Suicidality Subscale #' (DSA-SS) sum scores and past suicide attempts from 532 subjects are #' included as a demonstration set to calculate optimal cutpoints. Two #' additional demographic variables (age, gender) are also included to test #' for group differences. #' #' @format A data frame with 532 rows and 4 variables: #' \describe{ #' \item{age}{(numeric) Age of participants in years} #' \item{gender}{(factor) Gender} #' \item{dsi}{(numeric) Sum-score (0 = low suicidality, 12 = high suicidality)} #' \item{suicide}{(factor) Past suicide attempt (no = no attempt, yes = at least one attempt)} #' } #' @source von Glischinski, M., Teisman, T., Prinz, S., Gebauer, J., and Hirschfeld, G. (2017). Depressive Symptom Inventory- Suicidality Subscale: Optimal cut points for clinical and non-clinical samples. Clinical Psychology & Psychotherapy "suicide" #' Nodal involvement and acid phosphatase levels in 53 prostate cancer patients #' #' Prostatic acid phosphatase (PAP) emerged as the first clinically #' useful tumor marker in the 1940s and 1950s. This data set contains the #' serum levels of acid phosphatase of 53 patients that were confirmed to #' have prostate cancer and whether the neighboring lymph nodes were involved. #' #' @format A data frame with 53 rows and 2 variables: #' \describe{ #' \item{acid_phosphatase}{(numeric) Blood serum level of acid phosphatase} #' \item{nodal_involvement}{(logical) Whether neighboring lymph nodes were involved} #' } #' @source Le CT (2006). A solution for the most basic optimization problem associated with an ROC curve. Statistical methods in medical research 15: 571–584 "prostate_nodal"cutpointr/R/plot_metric_boot.R0000644000176200001440000000620114726066534016205 0ustar liggesusers#' Plot the bootstrapped metric distribution from a cutpointr object #' #' Given a \code{cutpointr} object this function plots the bootstrapped metric distribution, #' i.e. the distribution of out-of-bag metric values. #' The metric depends on the function that was supplied to \code{metric} in the #' call to \code{cutpointr}. #' The \code{cutpointr} function has to be run with \code{boot_runs}` > 0 to enable bootstrapping. #' @param x A cutpointr object. #' @param ... Additional arguments (unused) #' @examples #' set.seed(300) #' opt_cut <- cutpointr(suicide, dsi, suicide, boot_runs = 10) #' plot_metric_boot(opt_cut) #' @family cutpointr plotting functions #' @importFrom rlang .data #' @export plot_metric_boot <- function(x, ...) { args <- list(...) if (!(has_column(x, "subgroup"))) { dts_boot <- "boot" dts <- "data" transparency <- 1 } else { dts_boot <- c("boot", "subgroup") dts <- c("data", "subgroup") transparency <- 0.6 } if (has_boot_results(x)) { res_boot_unnested <- x %>% dplyr::select(dts_boot) %>% dplyr::mutate(boot = prepare_bind_rows(.data$boot)) %>% tidyr::unnest(.data$boot) %>% dplyr::select(-c("roc_curve_b", "roc_curve_oob")) # If multiple optimal cutpoints optimal_cutpoint is a list if (is.list(res_boot_unnested$optimal_cutpoint)) { res_boot_unnested <- res_boot_unnested %>% tidyr::unnest() } metric_name <- colnames(res_boot_unnested)[5] stopifnot(grepl(pattern = "_oob", x = metric_name)) if (all(na_inf_omit(get(metric_name, res_boot_unnested) %% 1 == 0)) | only_one_unique(na_inf_omit(get(metric_name, res_boot_unnested)))) { all_integer = TRUE dist_plot <- ggplot2::geom_bar(alpha = 1, position = "dodge") } else { all_integer = FALSE dist_plot <- ggplot2::geom_density(alpha = transparency) } if (!(has_column(x, "subgroup"))) { boot_metric <- suppressMessages( ggplot2::ggplot(res_boot_unnested, ggplot2::aes(x = !!rlang::ensym(metric_name))) + dist_plot + ggplot2::ggtitle("Bootstrap", "out-of-bag estimates") + ggplot2::xlab(metric_name) ) } else { boot_metric <- suppressMessages( ggplot2::ggplot(res_boot_unnested, ggplot2::aes(x = !!rlang::ensym(metric_name), fill = subgroup, color = subgroup)) + dist_plot + ggplot2::ggtitle("Bootstrap", "out-of-bag estimates") + ggplot2::xlab(metric_name) ) } if (!all_integer) boot_metric <- boot_metric + ggplot2::geom_rug(alpha = 0.5) } else { stop("No bootstrap results found. Was boot_runs > 0 in cutpointr?") } return(boot_metric) } cutpointr/R/optimize_metric.R0000644000176200001440000006026614726126671016056 0ustar liggesusersoptimize_metric <- function(data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, minmax, direction, metric_name = "metric", loess = FALSE, gam = FALSE, return_roc = TRUE, tol_metric, use_midpoints, ...) { args <- list(...) metric_name_call <- as.character(substitute(metric_func)) if (metric_name_call != "metric_func") metric_name <- metric_name_call roccurve <- roc(data = data, x = !!x, class = !!class, pos_class = pos_class, neg_class = neg_class, direction = direction) m <- metric_func(tp = roccurve[["tp"]], fp = roccurve[["fp"]], tn = roccurve[["tn"]], fn = roccurve[["fn"]], .roc_curve = roccurve, ...) m <- sanitize_metric(m, m_name = metric_name, n = nrow(roccurve)) roccurve$m <- as.numeric(m) if (!is.null(colnames(m))) metric_name <- colnames(m) if (loess) { roccurve$m_unsmoothed <- roccurve$m finite_x <- is.finite(roccurve$x.sorted) finite_m <- is.finite(roccurve$m) finite_roc <- (finite_x + finite_m) == 2 mod <- fANCOVA::loess.as(x = roccurve$x.sorted[finite_roc], y = roccurve$m[finite_roc], criterion = args$criterion, degree = args$degree, family = args$family, user.span = args$user.span) roccurve$m <- NA roccurve$m[finite_roc] <- mod$fitted } if (gam) { roccurve$m_unsmoothed <- roccurve$m finite_x <- is.finite(roccurve$x.sorted) finite_m <- is.finite(roccurve$m) finite_roc <- (finite_x + finite_m) == 2 mod <- mgcv::gam(stats::as.formula(paste(paste0(args$formula[2], " ~"), args$formula[3])), data = roccurve[finite_roc, ], optimizer = args[["optimizer"]]) roccurve$m <- NA roccurve$m[finite_roc] <- mod$fitted.values } if (minmax == "max") { m_vals <- stats::na.omit(roccurve$m) if (length(m_vals) == 0) stop("All metric values are missing.") max_m <- max(m_vals) opt <- which((roccurve$m >= (max_m - tol_metric)) & (roccurve$m <= (max_m + tol_metric))) oc <- roccurve[["x.sorted"]][opt] m_oc <- max_m } else if (minmax == "min") { m_vals <- stats::na.omit(roccurve$m) if (length(m_vals) == 0) stop("All metric values are missing.") min_m <- min(m_vals) opt <- which((roccurve$m >= (min_m - tol_metric)) & (roccurve$m <= (min_m + tol_metric))) oc <- roccurve[["x.sorted"]][opt] m_oc <- min_m } if (length(oc) > 1) { res <- tibble::tibble(optimal_cutpoint = list(oc)) if (use_midpoints) { res$optimal_cutpoint <- list(midpoint(oc = unlist(res$optimal_cutpoint), x = unlist(data[, x], use.names = FALSE), direction = direction)) } } else { res <- tibble::tibble(optimal_cutpoint = oc) if (use_midpoints) { res$optimal_cutpoint <- midpoint(oc = unlist(res$optimal_cutpoint), x = unlist(data[, x], use.names = FALSE), direction = direction) } } if (return_roc) { res$roc_curve <- list(roccurve) } if (loess) metric_name <- paste0("loess_", metric_name) if (gam) metric_name <- paste0("gam_", metric_name) res[, metric_name] <- m_oc return(res) } #' Optimize a metric function in binary classification #' #' Given a function for computing a metric in \code{metric_func}, these functions #' maximize or minimize that metric by selecting an optimal cutpoint. #' The metric function should accept the following inputs: #' \itemize{ #' \item \code{tp}: vector of number of true positives #' \item \code{fp}: vector of number of false positives #' \item \code{tn}: vector of number of true negatives #' \item \code{fn}: vector of number of false negatives #' } #' #' The above inputs are arrived at by using all unique values in \code{x}, Inf, or #' -Inf as possible cutpoints for classifying the variable in class. #' #' @return A tibble with the columns \code{optimal_cutpoint}, the corresponding metric #' value and \code{roc_curve}, a nested tibble that includes all possible cutoffs #' and the corresponding numbers of true and false positives / negatives and #' all corresponding metric values. #' #' @inheritParams oc_youden_normal #' @param metric_func (function) A function that computes a #' metric to be maximized. See description. #' @param tol_metric All cutpoints will be returned that lead to a metric #' value in the interval [m_max - tol_metric, m_max + tol_metric] where #' m_max is the maximum achievable metric value. This can be used to return #' multiple decent cutpoints and to avoid floating-point problems. #' @param use_midpoints (logical) If TRUE (default FALSE) the returned optimal #' cutpoint will be the mean of the optimal cutpoint and the next highest #' observation (for direction = ">") or the next lowest observation #' (for direction = "<") which avoids biasing the optimal cutpoint. #' @param ... Further arguments that will be passed to \code{metric_func}. #' @examples #' cutpointr(suicide, dsi, suicide, method = maximize_metric, metric = accuracy) #' @name maximize_metric #' @family method functions #' @export maximize_metric <- function(data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, tol_metric, use_midpoints, ...) { metric_name <- as.character(substitute(metric_func)) optimize_metric(data = data, x = x, class = class, metric_func = metric_func, pos_class = pos_class, neg_class = neg_class, minmax = "max", direction = direction, metric_name = metric_name, tol_metric = tol_metric, use_midpoints = use_midpoints, ...) } #' @examples #' cutpointr(suicide, dsi, suicide, method = minimize_metric, metric = abs_d_sens_spec) #' @rdname maximize_metric #' @export minimize_metric <- function(data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, tol_metric, use_midpoints, ...) { metric_name <- as.character(substitute(metric_func)) optimize_metric(data = data, x = x, class = class, metric_func = metric_func, pos_class = pos_class, neg_class = neg_class, minmax = "min", direction = direction, metric_name = metric_name, tol_metric = tol_metric, use_midpoints = use_midpoints, ...) } #' Optimize a metric function in binary classification after LOESS smoothing #' #' Given a function for computing a metric in \code{metric_func}, these functions #' smooth the function of metric value per cutpoint using LOESS, then #' maximize or minimize the metric by selecting an optimal cutpoint. For further details #' on the LOESS smoothing see \code{?fANCOVA::loess.as}. #' The \code{metric} function should accept the following inputs: #' \itemize{ #' \item \code{tp}: vector of number of true positives #' \item \code{fp}: vector of number of false positives #' \item \code{tn}: vector of number of true negatives #' \item \code{fn}: vector of number of false negatives #' } #' #' The above inputs are arrived at by using all unique values in \code{x}, Inf, and #' -Inf as possible cutpoints for classifying the variable in class. #' #' @return A tibble with the columns \code{optimal_cutpoint}, the corresponding metric #' value and \code{roc_curve}, a nested tibble that includes all possible cutoffs #' and the corresponding numbers of true and false positives / negatives and #' all corresponding metric values. #' #' @inheritParams oc_youden_normal #' @param metric_func (function) A function that computes a #' metric to be maximized. See description. #' @param criterion the criterion for automatic smoothing parameter selection: #' "aicc" denotes bias-corrected AIC criterion, "gcv" denotes generalized #' cross-validation. #' @param degree the degree of the local polynomials to be used. It can be #' 0, 1 or 2. #' @param family if "gaussian" fitting is by least-squares, and if "symmetric" #' a re-descending M estimator is used with Tukey's biweight function. #' @param user.span The user-defined parameter which controls the degree of #' smoothing #' @param tol_metric All cutpoints will be returned that lead to a metric #' value in the interval [m_max - tol_metric, m_max + tol_metric] where #' m_max is the maximum achievable metric value. This can be used to return #' multiple decent cutpoints and to avoid floating-point problems. #' @param use_midpoints (logical) If TRUE (default FALSE) the returned optimal #' cutpoint will be the mean of the optimal cutpoint and the next highest #' observation (for direction = ">") or the next lowest observation #' (for direction = "<") which avoids biasing the optimal cutpoint. #' @param ... Further arguments that will be passed to metric_func or the #' loess smoother. #' #' @source Xiao-Feng Wang (2010). fANCOVA: Nonparametric Analysis of Covariance. #' https://CRAN.R-project.org/package=fANCOVA #' @source Leeflang, M. M., Moons, K. G., Reitsma, J. B., & Zwinderman, A. H. #' (2008). Bias in sensitivity and specificity caused by data-driven selection #' of optimal cutoff values: mechanisms, magnitude, and solutions. #' Clinical Chemistry, (4), 729–738. #' @examples #' oc <- cutpointr(suicide, dsi, suicide, gender, method = maximize_loess_metric, #' criterion = "aicc", family = "symmetric", degree = 2, user.span = 0.7, #' metric = accuracy) #' plot_metric(oc) #' @name maximize_loess_metric #' @family method functions #' @export maximize_loess_metric <- function(data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, criterion = "aicc", degree = 1, family = "symmetric", user.span = NULL, tol_metric, use_midpoints, ...) { if (!requireNamespace("fANCOVA", quietly = TRUE)) { stop("fANCOVA package has to be installed to use LOESS smoothing.") } metric_name <- as.character(substitute(metric_func)) if (is.function(user.span)) { us <- user.span(data = data, x = x) } else { us <- user.span } optimize_metric(data = data, x = x, class = class, metric_func = metric_func, pos_class = pos_class, neg_class = neg_class, minmax = "max", direction = direction, metric_name = metric_name, criterion = criterion, degree = degree, family = family, user.span = us, loess = TRUE, tol_metric = tol_metric, use_midpoints = use_midpoints, ...) } #' @examples #' oc <- cutpointr(suicide, dsi, suicide, gender, method = minimize_loess_metric, #' criterion = "aicc", family = "symmetric", degree = 2, user.span = 0.7, #' metric = misclassification_cost, cost_fp = 1, cost_fn = 10) #' plot_metric(oc) #' @rdname maximize_loess_metric #' @export minimize_loess_metric <- function(data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, criterion = "aicc", degree = 1, family = "symmetric", user.span = NULL, tol_metric, use_midpoints, ...) { if (!requireNamespace("fANCOVA", quietly = TRUE)) { stop("fANCOVA package has to be installed to use LOESS smoothing.") } metric_name <- as.character(substitute(metric_func)) if (is.function(user.span)) { us <- user.span(data = data, x = x) } else { us <- user.span } optimize_metric(data = data, x = x, class = class, metric_func = metric_func, pos_class = pos_class, neg_class = neg_class, minmax = "min", direction = direction, metric_name = metric_name, criterion = criterion, degree = degree, family = family, loess = TRUE, user.span = us, tol_metric = tol_metric, use_midpoints = use_midpoints, ...) } #' Calculate bandwidth for LOESS smoothing of metric functions by rule of thumb #' #' This function implements a rule of thumb for selecting the bandwidth when #' smoothing a function of metric values per cutpoint value, particularly #' in \code{maximize_loess_metric} and \code{minimize_loess_metric}. #' #' The function used for calculating the bandwidth is 0.1 * xsd / sqrt(xn), #' where xsd is the standard deviation of the unique values of the predictor #' variable (i.e. all cutpoints) and xn is the number of unique predictor values. #' #' @param data A data frame #' @param x The predictor variable user_span_cutpointr <- function(data, x) { finite_x <- is.finite(data[[x]]) xsd <- stats::sd(data[[x]][finite_x], na.rm = TRUE) xn <- length(unique(data[[x]][finite_x])) print(0.1 * xsd / sqrt(xn)) return(0.1 * xsd / sqrt(xn)) } #' Optimize a metric function in binary classification after bootstrapping #' #' Given a function for computing a metric in \code{metric_func}, these functions #' bootstrap the data \code{boot_cut} times and #' maximize or minimize the metric by selecting an optimal cutpoint. The returned #' optimal cutpoint is the result of applying \code{summary_func}, e.g. the mean, #' to all optimal cutpoints that were determined in the bootstrap samples. #' The \code{metric} function should accept the following inputs: #' \itemize{ #' \item \code{tp}: vector of number of true positives #' \item \code{fp}: vector of number of false positives #' \item \code{tn}: vector of number of true negatives #' \item \code{fn}: vector of number of false negatives #' } #' #' The above inputs are arrived at by using all unique values in \code{x}, Inf, and #' -Inf as possible cutpoints for classifying the variable in class. #' The reported metric represents the usual in-sample performance of the #' determined cutpoint. #' #' @return A tibble with the column \code{optimal_cutpoint} #' #' @inheritParams oc_youden_normal #' @param metric_func (function) A function that computes a single number #' metric to be maximized. See description. #' @param summary_func (function) After obtaining the bootstrapped optimal #' cutpoints this function, e.g. mean or median, is applied to arrive at a single cutpoint. #' @param boot_cut (numeric) Number of bootstrap repetitions over which the mean #' optimal cutpoint is calculated. #' @param boot_stratify (logical) If the bootstrap is stratified, bootstrap #' samples are drawn in both classes and then combined, keeping the number of #' positives and negatives constant in every resample. #' @param inf_rm (logical) whether to remove infinite cutpoints before #' calculating the summary. #' @param tol_metric All cutpoints will be passed to \code{summary_func} #' that lead to a metric #' value in the interval [m_max - tol_metric, m_max + tol_metric] where #' m_max is the maximum achievable metric value. This can be used to return #' multiple decent cutpoints and to avoid floating-point problems. #' @param use_midpoints (logical) If TRUE (default FALSE) the returned optimal #' cutpoint will be the mean of the optimal cutpoint and the next highest #' observation (for direction = ">") or the next lowest observation #' (for direction = "<") which avoids biasing the optimal cutpoint. #' #' @examples #' set.seed(100) #' cutpointr(suicide, dsi, suicide, method = maximize_boot_metric, #' metric = accuracy, boot_cut = 30) #' @name maximize_boot_metric #' @family method functions #' @export maximize_boot_metric <- function(data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, summary_func = mean, boot_cut = 50, boot_stratify, inf_rm = TRUE, tol_metric, use_midpoints, ...) { metric_name <- as.character(substitute(metric_func)) if (boot_stratify) { ind_pos <- which(unlist(data[, class]) == pos_class) ind_neg <- which(unlist(data[, class]) == neg_class) } else { ind_pos <- NA ind_neg <- NA } optimal_cutpoints <- purrr::map(1:boot_cut, function(i) { b_ind <- simple_boot(data = data, ind_pos = ind_pos, ind_neg = ind_neg, dep_var = class, stratify = boot_stratify) opt_cut <- optimize_metric(data = data[b_ind, ], x = x, class = class, metric_func = metric_func, pos_class = pos_class, neg_class = neg_class, minmax = "max", direction = direction, metric_name = metric_name, return_roc = FALSE, tol_metric = tol_metric, use_midpoints = use_midpoints, ...) return(unlist(opt_cut$optimal_cutpoint)) }) optimal_cutpoints <- unlist(optimal_cutpoints) if (inf_rm) optimal_cutpoints <- optimal_cutpoints[is.finite(optimal_cutpoints)] return(data.frame(optimal_cutpoint = summary_func(optimal_cutpoints))) } #' @rdname maximize_boot_metric #' @examples #' set.seed(100) #' cutpointr(suicide, dsi, suicide, method = minimize_boot_metric, #' metric = abs_d_sens_spec, boot_cut = 30) #' @export minimize_boot_metric <- function(data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, summary_func = mean, boot_cut = 50, boot_stratify, inf_rm = TRUE, tol_metric, use_midpoints, ...) { metric_name <- as.character(substitute(metric_func)) if (boot_stratify) { ind_pos <- which(unlist(data[, class]) == pos_class) ind_neg <- which(unlist(data[, class]) == neg_class) } else { ind_pos <- NA ind_neg <- NA } optimal_cutpoints <- purrr::map(1:boot_cut, function(i) { b_ind <- simple_boot(data = data, ind_pos = ind_pos, ind_neg = ind_neg, dep_var = class, stratify = boot_stratify) opt_cut <- optimize_metric(data = data[b_ind, ], x = x, class = class, metric_func = metric_func, pos_class = pos_class, neg_class = neg_class, minmax = "min", direction = direction, metric_name = metric_name, return_roc = FALSE, tol_metric = tol_metric, use_midpoints = use_midpoints, ...) return(unlist(opt_cut$optimal_cutpoint)) }) optimal_cutpoints <- unlist(optimal_cutpoints) if (inf_rm) optimal_cutpoints <- optimal_cutpoints[is.finite(optimal_cutpoints)] return(data.frame(optimal_cutpoint = summary_func(optimal_cutpoints))) } #' Optimize a metric function in binary classification after smoothing via #' generalized additive models #' #' Given a function for computing a metric in \code{metric_func}, these functions #' smooth the function of metric value per cutpoint using generalized additive #' models (as implemented in \pkg{mgcv}), then #' maximize or minimize the metric by selecting an optimal cutpoint. For further details #' on the GAM smoothing see \code{?mgcv::gam}. #' The \code{metric} function should accept the following inputs: #' \itemize{ #' \item \code{tp}: vector of number of true positives #' \item \code{fp}: vector of number of false positives #' \item \code{tn}: vector of number of true negatives #' \item \code{fn}: vector of number of false negatives #' } #' #' The above inputs are arrived at by using all unique values in \code{x}, Inf, and #' -Inf as possible cutpoints for classifying the variable in class. #' #' @return A tibble with the columns \code{optimal_cutpoint}, the corresponding metric #' value and \code{roc_curve}, a nested tibble that includes all possible cutoffs #' and the corresponding numbers of true and false positives / negatives and #' all corresponding metric values. #' #' @inheritParams oc_youden_normal #' @param metric_func (function) A function that computes a #' metric to be maximized. See description. #' @param formula A GAM formula. See \code{help("gam", package = "mgcv")} for #' details. #' @param optimizer An array specifying the numerical optimization method to #' use to optimize the smoothing parameter estimation criterion (given by method). #' See \code{help("gam", package = "mgcv")} for details. #' @param tol_metric All cutpoints will be returned that lead to a metric #' value in the interval [m_max - tol_metric, m_max + tol_metric] where #' m_max is the maximum achievable metric value. This can be used to return #' multiple decent cutpoints and to avoid floating-point problems. #' @param use_midpoints (logical) If TRUE (default FALSE) the returned optimal #' cutpoint will be the mean of the optimal cutpoint and the next highest #' observation (for direction = ">") or the next lowest observation #' (for direction = "<") which avoids biasing the optimal cutpoint. #' @param ... Further arguments that will be passed to metric_func or the #' GAM smoother. #' #' @examples #' oc <- cutpointr(suicide, dsi, suicide, gender, method = maximize_gam_metric, #' metric = accuracy) #' plot_metric(oc) #' @name maximize_gam_metric #' @family method functions #' @export maximize_gam_metric <- function(data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, formula = m ~ s(x.sorted), optimizer = c("outer", "newton"), tol_metric, use_midpoints, ...) { if (!requireNamespace("mgcv", quietly = TRUE)) { stop("mgcv package has to be installed to use GAM smoothing.") } formula <- as.character(formula) metric_name <- as.character(substitute(metric_func)) optimize_metric(data = data, x = x, class = class, metric_func = metric_func, pos_class = pos_class, neg_class = neg_class, minmax = "max", direction = direction, metric_name = metric_name, formula = formula, optimizer = optimizer, gam = TRUE, tol_metric = tol_metric, use_midpoints = use_midpoints, ...) } #' @examples #' oc <- cutpointr(suicide, dsi, suicide, gender, method = minimize_gam_metric, #' metric = abs_d_sens_spec) #' plot_metric(oc) #' @rdname maximize_gam_metric #' @export minimize_gam_metric <- function(data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, formula = m ~ s(x.sorted), optimizer = c("outer", "newton"), tol_metric, use_midpoints, ...) { if (!requireNamespace("mgcv", quietly = TRUE)) { stop("mgcv package has to be installed to use GAM smoothing.") } metric_name <- as.character(substitute(metric_func)) optimize_metric(data = data, x = x, class = class, metric_func = metric_func, pos_class = pos_class, neg_class = neg_class, minmax = "min", direction = direction, metric_name = metric_name, formula = formula, optimizer = optimizer, gam = TRUE, tol_metric = tol_metric, use_midpoints = use_midpoints, ...) }cutpointr/R/add_metric.R0000644000176200001440000001715014726066534014741 0ustar liggesusers#' Add metrics to a cutpointr or roc_cutpointr object #' #' By default, the output of cutpointr includes the optimized metric and several #' other metrics. This function adds further metrics. Suitable metric functions #' are all metric functions that are included in the package or that comply #' with those standards. #' #' @param object A cutpointr or roc_cutpointr object. #' @param metric (list) A list of metric functions to be added. #' @return A cutpointr or roc_cutpointr object (a data.frame) with one or more added columns. #' @examples #' library(dplyr) #' library(cutpointr) #' cutpointr(suicide, dsi, suicide, gender) %>% #' add_metric(list(ppv, npv)) %>% #' select(optimal_cutpoint, subgroup, AUC, sum_sens_spec, ppv, npv) #' @name add_metric #' @export #' @family main cutpointr functions #' @importFrom rlang !! := #' add_metric <- function(object, metric) { UseMethod("add_metric", object) } #' @rdname add_metric #' @export add_metric.cutpointr <- function(object, metric) { if (!is.list(metric)) stop("The metric function(s) must be given as a list.") met <- purrr::map(metric, function(metric_func) { if (!is.function(metric_func)) { stop("The list elements of metric have to be functions.") } # Get numbers of TP, FP, TN, FN at optimal cutpoint(s) from ROC curve(s) # and calculate the metric(s). Allow for type instability of the metric func: one_met <- purrr::pmap_df(list(object$optimal_cutpoint, object$roc_curve, object$direction), function(optimal_cutpoint, roc_curve, direction) { opt_ind <- get_opt_ind(roc_curve = roc_curve, oc = optimal_cutpoint, direction = direction) met <- metric_func(tp = roc_curve$tp[opt_ind], fp = roc_curve$fp[opt_ind], tn = roc_curve$tn[opt_ind], fn = roc_curve$fn[opt_ind]) met_name <- colnames(met) if (length(met_name) > 1) { stop("The metric function should return one column or a vector.") } if (is.null(met_name)) met_name <- "added_metric" if (length(met) > 1) { met <- list(as.numeric(unlist(met))) } else { met <- as.numeric(met) } tibble::tibble(!!met_name := met) }) class(one_met) <- class(object) return(one_met) }) oc <- dplyr::bind_cols(object, met) if (has_boot_results(oc)) { boot_augmented <- purrr::map(oc$boot, function(boot_group) { add_per_subgroup <- purrr::map(metric, function(metric_func) { metric_robust <- function(metric_func, tp, fp, tn, fn) { tp <- unlist(tp) fp <- unlist(fp) tn <- unlist(tn) fn <- unlist(fn) met <- as.numeric(metric_func(tp = tp, fp = fp, tn = tn, fn = fn)) if (length(met) > 1) met <- list(met) return(met) } one_met <- purrr::map(1:nrow(boot_group), function(r) { met_b <- metric_robust(metric_func = metric_func, tp = boot_group$TP_b[r], fp = boot_group$FP_b[r], tn = boot_group$TN_b[r], fn = boot_group$FN_b[r]) met_oob <- metric_robust(metric_func = metric_func, tp = boot_group$TP_oob[r], fp = boot_group$FP_oob[r], tn = boot_group$TN_oob[r], fn = boot_group$FN_oob[r]) tibble::tibble(met_b, met_oob) }) # Bind rows allowing for multiple optimal cutpoints has_multiple <- any( purrr::map_lgl(one_met, function(x) { length(unlist(x)) > 2 }) ) if (has_multiple) { one_met <- purrr::map_df(one_met, function(x) { if (length(unlist(x)) == 2) { tibble::tibble(met_b = list(x$met_b), met_oob = list(x$met_oob)) } else { tibble::tibble(met_b = x$met_b, met_oob = x$met_oob) } }) } else { one_met <- dplyr::bind_rows(one_met) } met_name <- colnames(metric_func(1,1,1,1)) # just get the name... if (length(met_name) > 1) { stop("The metric function should return one column or a vector.") } if (is.null(met_name)) met_name <- "added_metric" colnames(one_met) <- paste0(met_name, c("_b", "_oob")) return(one_met) }) add_per_subgroup <- dplyr::bind_cols(add_per_subgroup) boot_group <- dplyr::bind_cols(boot_group, add_per_subgroup) return(boot_group) }) oc$boot <- boot_augmented } return(oc) } #' @rdname add_metric #' @export add_metric.multi_cutpointr <- function(object, metric) { orig_class <- class(object) class(object) <- c("cutpointr", class(object)) res <- add_metric(object, metric) class(res) <- orig_class return(res) } #' @rdname add_metric #' @export add_metric.roc_cutpointr <- function(object, metric) { if (!is.list(metric)) stop("The metric function(s) must be given as a list.") met <- purrr::map(metric, function(metric_func) { if (!is.function(metric_func)) { stop("The list elements of metric have to be functions.") } # Get numbers of TP, FP, TN, FN at optimal cutpoint(s) from ROC curve(s) # and calculate the metric(s). Allow for type instability of the metric func: met <- metric_func(tp = object$tp, fp = object$fp, tn = object$tn, fn = object$fn) met_name <- colnames(met) if (length(met_name) > 1) { stop("The metric function should return one column or a vector.") } if (is.null(met_name)) met_name <- "added_metric" met <- as.numeric(met) met <- tibble::tibble(!!met_name := met) class(met) <- class(object) return(met) }) roc_added <- dplyr::bind_cols(object, met) return(roc_added) }cutpointr/R/print.summary_multi_cutpointr.R0000644000176200001440000000533414726066534021020 0ustar liggesusers#' @export print.summary_multi_cutpointr <- function(x, digits = 4, ...) { cat(paste("Method:", x$cutpointr[[1]]$method, "\n")) cat(paste("Predictor:", paste(unique(purrr::map_chr(x$cutpointr, function(x) x$predictor)), collapse = ", "), "\n")) cat(paste("Outcome:", x$cutpointr[[1]]$outcome, "\n")) if (has_column(x$cutpointr[[1]], "subgroup")) { cat(c("Subgroups:", paste(unique(purrr::map(x$cutpointr, ~ .$subgroup)), collapse = ", "), "\n")) } if (has_boot_results(x)) { cat(paste("Nr. of bootstraps:", x$boot_runs[1], "\n")) } for (i in 1:nrow(x)) { cat("\n") cat(paste("Predictor:", x$cutpointr[[i]]$predictor, "\n")) if (has_column(x$cutpointr[[i]], "subgroup")) { cat(paste("Subgroup:", x$cutpointr[[i]]$subgroup, "\n")) } cat(paste0(rep("-", getOption("width")), collapse = ""), "\n") x$cutpointr[[i]] %>% dplyr::select(.data$direction, .data$AUC) %>% # round(digits = digits) %>% dplyr::mutate(AUC = round(AUC, digits = digits), n = x$n_obs[i], n_pos = x$n_pos[i], n_neg = x$n_neg[i]) %>% as.data.frame %>% print_df_nodat() cat("\n") purrr::map_df(1:length(x$cutpointr[[i]]$optimal_cutpoint[[1]]), function(j) { x$cutpointr[[i]] %>% dplyr::select(# .data$direction, .data$optimal_cutpoint, !!find_metric_name(x$cutpointr[[i]]), .data$acc, .data$sensitivity, .data$specificity) %>% purrr::map_df(get_fnth, n = j) }) %>% as.data.frame %>% dplyr::left_join(y = x$confusion_matrix[[i]], by = c("optimal_cutpoint" = "cutpoint")) %>% dplyr::mutate_if(is.numeric, round, digits = digits) %>% print_df_nodat() cat("\n") cat(paste("Predictor summary:", "\n")) dplyr::bind_rows(x$desc[[i]], x$desc_by_class[[i]]) %>% dplyr::mutate_if(is.numeric, function(x) round(x, digits)) %>% print_df_nodat() if (has_boot_results(x[i, ])) { cat("\n") cat(paste("Bootstrap summary:", "\n")) print_df_nodat( x[["boot"]][[i]] %>% dplyr::mutate_if(is.numeric, round, digits = 2), row.names = rep("", nrow(x[["boot"]][[i]])) ) } } return(invisible(x)) } cutpointr/R/summary.cutpointr.R0000644000176200001440000000650514726066534016373 0ustar liggesusers#' @export summary.cutpointr <- function(object, ...) { x_summary <- vector("list", nrow(object)) names(x_summary) <- suppressWarnings(object$subgroup) for (r in 1:nrow(object)) { temprow <- object[r, ] if (has_column(object, "subgroup")) { x_summary[[r]]$subgroup <- temprow$subgroup } x_summary[[r]]$cutpointr <- temprow x_summary[[r]]$desc <- dplyr::bind_cols( tibble::tibble(Data = "Overall"), summary_sd_df(temprow$data[[1]] %>% dplyr::select(temprow$predictor)) ) temprow_split <- temprow$data[[1]] %>% split(temprow$data[[1]][, temprow$outcome]) x_summary[[r]]$desc_byclass <- temprow_split %>% purrr::map_df(function(x) { summary_sd_df(x %>% dplyr::select(temprow$predictor)) }) x_summary[[r]]$desc_byclass <- dplyr::bind_cols( data.frame(Data = names(temprow_split), stringsAsFactors = FALSE), x_summary[[r]]$desc_byclass ) x_summary[[r]]$n_obs <- nrow(temprow$data[[1]]) x_summary[[r]]$n_pos <- temprow$data[[1]] %>% dplyr::select(temprow$outcome) %>% unlist %>% (function(x) sum(x == temprow$pos_class)) x_summary[[r]]$n_neg <- x_summary[[r]]$n_obs - x_summary[[r]]$n_pos # Confusion Matrix oi <- get_opt_ind(temprow$roc_curve[[1]], oc = unlist(temprow$optimal_cutpoint), direction = temprow$direction) x_summary[[r]]$confusion_matrix <- data.frame( cutpoint = unlist(temprow$optimal_cutpoint), temprow$roc_curve[[1]][oi, c("tp", "fn", "fp", "tn")] ) if (has_boot_results(temprow)) { x_summary[[r]][["boot"]] <- temprow[["boot"]][[1]] %>% dplyr::select(-(TP_b:roc_curve_oob)) %>% purrr::map(function(x) { summary_sd(x) }) x_summary[[r]][["boot"]] <- do.call(rbind, x_summary[[r]][["boot"]]) x_summary[[r]][["boot"]] <- as.data.frame(x_summary[[r]][["boot"]]) x_summary[[r]][["boot"]] <- tibble::rownames_to_column(x_summary[[r]][["boot"]], var = "Variable") x_summary[[r]]$boot_runs <- nrow(temprow[["boot"]][[1]]) } } x_summary <- purrr::map_df(x_summary, tidy_summary) class(x_summary) <- c("summary_cutpointr", class(x_summary)) return(x_summary) } # Convert the list output of summary.cutpointr to a data.frame # x is a single element of the resulting list from summary.cutpointr. tidy_summary <- function(x) { res <- tibble::tibble( cutpointr = list(x$cutpointr), desc = list(x$desc), desc_by_class = list(x$desc_byclass), n_obs = x$n_obs, n_pos = x$n_pos, n_neg = x$n_neg, confusion_matrix = list(x$confusion_matrix) ) if (has_boot_results(x)) { res <- dplyr::bind_cols(res, tibble::tibble(boot = list(x[["boot"]])), boot_runs = x$boot_runs) } if (has_column(x, "subgroup")) { res <- dplyr::bind_cols(subgroup = x$subgroup, res) } return(res) }cutpointr/R/globals.R0000644000176200001440000000053114726066534014264 0ustar liggesusersutils::globalVariables(c("x.sorted", "m", "subgroup", "Sensitivity", "Specificity", "value", "metric", "Precision", "Recall", "optimal_cutpoint", "ymin", "ymax", "m_unsmoothed", "direction", "method", "AUC", "boot", "TP_b", "roc_curve_oob"))cutpointr/vignettes/0000755000176200001440000000000014726136577014333 5ustar liggesuserscutpointr/vignettes/cutpointr_plotting.Rmd0000644000176200001440000001052614726066534020745 0ustar liggesusers--- title: "Plotting with cutpointr" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Plotting with cutpointr} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` **cutpointr** includes several convenience functions for plotting data from a `cutpointr` object. These include: - `plot_cutpointr`: General purpose plotting function for cutpointr or roc_cutpointr objects - `plot_cut_boot`: Plot the bootstrapped distribution of optimal cutpoints - `plot_metric`: If `maximize_metric` or `minimize_metric` was used this function plots all possible cutoffs on the x-axis vs. the respective metric values on the y-axis. If bootstrapping was run, a confidence interval based on the bootstrapped distribution of metric values at each cutpoint can be displayed. To display no confidence interval set `conf_lvl = 0`. - `plot_metric_boot`: Plot the distribution of out-of-bag metric values - `plot_precision_recall`: Plot the precision recall curve - `plot_sensitivity_specificity`: Plot all cutpoints vs. sensitivity and specificity - `plot_roc`: Plot the ROC curve - `plot_x`: Plot the distribution of the predictor variable ```{r, fig.width=4, fig.height=3} library(cutpointr) set.seed(123) opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500) plot_cut_boot(opt_cut_b_g) plot_metric(opt_cut_b_g, conf_lvl = 0.9) plot_metric_boot(opt_cut_b_g) plot_precision_recall(opt_cut_b_g) plot_sensitivity_specificity(opt_cut_b_g) plot_roc(opt_cut_b_g) ``` All plot functions, except for the standard plot method that returns a composed plot, return `ggplot` objects than can be further modified. For example, changing labels, title, and the theme can be achieved this way: ```{r, fig.width=4, fig.height=3} library(ggplot2) p <- plot_x(opt_cut_b_g) p + ggtitle("Distribution of dsi") + theme_minimal() + xlab("Depression score") ``` ## Flexible plotting function Using `plot_cutpointr` any metric can be chosen to be plotted on the x- or y-axis and results of `cutpointr()` as well as `roc()` can be plotted. If a `cutpointr` object is to be plotted, it is thus irrelevant which `metric` function was chosen for cutpoint estimation. Any metric that can be calculated based on the ROC curve can be subsequently plotted as only the true / false positives / negatives over all cutpoints are needed. That way, not only the above plots can be produced, but also any combination of two metrics (or metric functions) and / or cutpoints. The built-in metric functions as well as user-defined functions or anonymous functions can be supplied to `xvar` and `yvar`. If bootstrapping was run, confidence intervals can be plotted around the y-variable. This is especially useful if the cutpoints, available in the `cutpoints` function, are placed on the x-axis. Note that confidence intervals can only be correctly plotted if the values of `xvar` are constant across bootstrap samples. For example, confidence intervals for TPR by FPR (a ROC curve) cannot be plotted easily, as the values of the false positive rate vary per bootstrap sample. ```{r, fig.width=4, fig.height=3, cache=FALSE} set.seed(1234) opt_cut_b <- cutpointr(suicide, dsi, suicide, boot_runs = 500) plot_cutpointr(opt_cut_b, xvar = cutpoints, yvar = sum_sens_spec, conf_lvl = 0.9) plot_cutpointr(opt_cut_b, xvar = fpr, yvar = tpr, aspect_ratio = 1, conf_lvl = 0) plot_cutpointr(opt_cut_b, xvar = cutpoint, yvar = tp, conf_lvl = 0.9) + geom_point() ``` ## Manual plotting Since `cutpointr` returns a `data.frame` with the original data, bootstrap results, and the ROC curve in nested tibbles, these data can be conveniently extracted and plotted manually. The relevant nested tibbles are in the columns `data`, `roc_curve` and `boot`. The following is an example of accessing and plotting the grouped data. ```{r, fig.width=4, fig.height=3} library(dplyr) library(tidyr) opt_cut_b_g |> select(data, subgroup) |> unnest(cols = data) |> ggplot(aes(x = suicide, y = dsi)) + geom_boxplot(alpha = 0.3) + facet_grid(~subgroup) ```cutpointr/vignettes/cutpointr_user_functions.Rmd0000644000176200001440000001006714726066534022153 0ustar liggesusers--- title: "User-defined functions for estimation methods and metrics" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{User-defined functions for estimation methods and metrics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` # User-defined functions ## method User-defined functions can be supplied to `method`, which is the function that is responsible for returning the optimal cutpoint. To define a new method function, create a function that may take as input(s): - `data`: A `data.frame` or `tbl_df` - `x`: (character) The name of the predictor variable - `class`: (character) The name of the class variable - `metric_func`: A function for calculating a metric, e.g. accuracy. Note that the method function does not necessarily have to accept this argument - `pos_class`: The positive class - `neg_class`: The negative class - `direction`: `">="` if the positive class has higher x values, `"<="` otherwise - `tol_metric`: (numeric) In the built-in methods, all cutpoints will be returned that lead to a metric value in the interval [m_max - tol_metric, m_max + tol_metric] where m_max is the maximum achievable metric value. This can be used to return multiple decent cutpoints and to avoid floating-point problems. - `use_midpoints`: (logical) In the built-in methods, if TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = ">") or the next lowest observation (for direction = "<") which avoids biasing the optimal cutpoint. - `...`: Further arguments that are passed to `metric` or that can be captured inside of `method` The function should return a data frame or tibble with one row, the column `optimal_cutpoint`, and an optional column with an arbitrary name with the metric value at the optimal cutpoint. For example, a function for choosing the cutpoint as the mean of the independent variable could look like this: ```{r, eval = FALSE} mean_cut <- function(data, x, ...) { oc <- mean(data[[x]]) return(data.frame(optimal_cutpoint = oc)) } ``` If a `method` function does not return a metric column, the default `sum_sens_spec`, the sum of sensitivity and specificity, is returned as the extra metric column in addition to accuracy, sensitivity and specificity. Some `method` functions that make use of the additional arguments (that are captured by `...`) are already included in **cutpointr**, see the list at the top. Since these functions are arguments to `cutpointr` their code can be accessed by simply typing their name, see for example `oc_youden_normal`. ## metric User defined `metric` functions can be used as well. They are mainly useful in conjunction with `method = maximize_metric`, `method = minimize_metric`, or one of the other minimization and maximization functions. In case of a different `method` function `metric` will only be used as the main out-of-bag metric when plotting the result. The `metric` function should accept the following inputs as vectors: - `tp`: Vector of true positives - `fp`: Vector of false positives - `tn`: Vector of true negatives - `fn`: Vector of false negatives - `...`: Further arguments The function should return a numeric vector, a matrix, or a `data.frame` with one column. If the column is named, the name will be included in the output and plots. Avoid using names that are identical to the column names that are by default returned by **cutpointr**, as such names will be prefixed by `metric_` in the output. The inputs (`tp`, `fp`, `tn`, and `fn`) are vectors. The code of the included metric functions can be accessed by simply typing their name. For example, this is the `misclassification_cost` metric function: ```{r} library(cutpointr) misclassification_cost ``` cutpointr/vignettes/cutpointr_roc.Rmd0000644000176200001440000000744414726066534017675 0ustar liggesusers--- title: "ROC curves with cutpointr" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{ROC curves with cutpointr} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` ## Calculating only the ROC curve When running `cutpointr`, a ROC curve is by default returned in the column `roc_curve`. This ROC curve can be plotted using `plot_roc`. Alternatively, if only the ROC curve is desired and no cutpoint needs to be calculated, the ROC curve can be created using `roc()` and plotted using `plot_cutpointr`. The `roc` function, unlike `cutpointr`, does not determine `direction`, `pos_class` or `neg_class` automatically. ```{r, fig.width=4, fig.height=3} library(cutpointr) roc_curve <- roc(data = suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") auc(roc_curve) head(roc_curve) plot_roc(roc_curve) ``` ## ROC curve and optimal cutpoint for multiple variables Alternatively, we can map the standard evaluation version `cutpointr` to the column names. If `direction` and / or `pos_class` and `neg_class` are unspecified, these parameters will automatically be determined by **cutpointr** so that the AUC values for all variables will be $> 0.5$. We could do this manually, e.g. using `purrr::map`, but to make this task more convenient `multi_cutpointr` can be used to achieve the same result. It maps multiple predictor columns to `cutpointr`, by default all numeric columns except for the class column. ```{r} mcp <- multi_cutpointr(suicide, class = suicide, pos_class = "yes", use_midpoints = TRUE, silent = TRUE) summary(mcp) ``` ## Accessing `data`, `roc_curve`, and `boot` The object returned by `cutpointr` is of the classes `cutpointr`, `tbl_df`, `tbl`, and `data.frame`. Thus, it can be handled like a usual data frame. The columns `data`, `roc_curve`, and `boot` consist of nested data frames, which means that these are list columns whose elements are data frames. They can either be accessed using `[` or by using functions from the tidyverse. If subgroups were given, the output contains one row per subgroup and the function that accesses the data should be mapped to every row or the data should be grouped by subgroup. ```{r, eval = FALSE, message = FALSE} set.seed(123) opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500) ``` ```{r, message = FALSE} library(dplyr) library(tidyr) opt_cut_b_g |> group_by(subgroup) |> select(subgroup, boot) |> unnest(cols = boot) |> summarise(sd_oc_boot = sd(optimal_cutpoint), m_oc_boot = mean(optimal_cutpoint), m_acc_oob = mean(acc_oob)) ``` ## Adding metrics to the result of cutpointr() or roc() By default, the output of `cutpointr` includes the optimized metric and several other metrics. The `add_metric` function adds further metrics. Here, we're adding the negative predictive value (NPV) and the positive predictive value (PPV) at the optimal cutpoint per subgroup: ```{r} cutpointr(suicide, dsi, suicide, gender, metric = youden, silent = TRUE) |> add_metric(list(ppv, npv)) |> select(subgroup, optimal_cutpoint, youden, ppv, npv) ``` In the same fashion, additional metric columns can be added to a `roc_cutpointr` object: ```{r} roc(data = suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") |> add_metric(list(cohens_kappa, F1_score)) |> select(x.sorted, tp, fp, tn, fn, cohens_kappa, F1_score) |> head() ``` cutpointr/vignettes/cutpointr_benchmarks.Rmd0000644000176200001440000003462714726066534021232 0ustar liggesusers--- title: "cutpointr benchmarks" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{cutpointr benchmarks} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` To offer a comparison to established solutions, **cutpointr** will be benchmarked against `optimal.cutpoints` from the **OptimalCutpoints** package, **ThresholdROC** and custom functions based on the **ROCR** and **pROC** packages. By generating data of different sizes the benchmarks will offer a comparison of the scalability of the different solutions. Using `prediction` and `performance` from the **ROCR** package and `roc` from the **pROC** package, we can write functions for computing the cutpoint that maximizes the sum of sensitivity and specificity. **pROC** has a built-in function to optimize a few metrics: ```{r, eval = FALSE} # Return cutpoint that maximizes the sum of sensitivity and specificiy # ROCR package rocr_sensspec <- function(x, class) { pred <- ROCR::prediction(x, class) perf <- ROCR::performance(pred, "sens", "spec") sens <- slot(perf, "y.values")[[1]] spec <- slot(perf, "x.values")[[1]] cut <- slot(perf, "alpha.values")[[1]] cut[which.max(sens + spec)] } # pROC package proc_sensspec <- function(x, class) { r <- pROC::roc(class, x, algorithm = 2, levels = c(0, 1), direction = "<") pROC::coords(r, "best", ret="threshold", transpose = FALSE)[1] } ``` The benchmarking will be carried out using the **microbenchmark** package and randomly generated data. The values of the `x` predictor variable are drawn from a normal distribution which leads to a lot more unique values than were encountered before in the `suicide` data. Accordingly, the search for an optimal cutpoint is much more demanding, if all possible cutpoints are evaluated. Benchmarks are run for sample sizes of 100, 1000, 1e4, 1e5, 1e6, and 1e7. For low sample sizes **cutpointr** is slower than the other solutions. While this should be of low practical importance, **cutpointr** scales more favorably with increasing sample size. The speed disadvantage in small samples that leads to the lower limit of around 25ms is mainly due to the nesting of the original data and the results that makes the compact output of `cutpointr` possible. This observation is emphasized by the fact that `cutpointr::roc` is quite fast also in small samples. For sample sizes > 1e5 **cutpointr** is a little faster than the function based on **ROCR** and **pROC**. Both of these solutions are generally faster than **OptimalCutpoints** and **ThresholdROC** with the exception of small samples. **OptimalCutpoints** and **ThresholdROC** had to be excluded from benchmarks with more than 1e4 observations due to high memory requirements and/or excessive run times, rendering the use of these packages in larger samples impractical. ```{r, eval = FALSE, echo = FALSE} library(OptimalCutpoints) library(ThresholdROC) library(dplyr) n <- 100 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) x_pos <- dat$x[dat$y == 1] x_neg <- dat$x[dat$y == 0] bench_100 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), optimal.cutpoints(X = "x", status = "y", tag.healthy = 0, methods = "Youden", data = dat), thres2(k1 = x_neg, k2 = x_pos, rho = 0.5, method = "empirical", ci = FALSE), times = 100, unit = "ms" ) n <- 1000 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) x_pos <- dat$x[dat$y == 1] x_neg <- dat$x[dat$y == 0] bench_1000 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), optimal.cutpoints(X = "x", status = "y", tag.healthy = 0, methods = "Youden", data = dat), thres2(k1 = x_neg, k2 = x_pos, rho = 0.5, method = "empirical", ci = FALSE), times = 100, unit = "ms" ) n <- 10000 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) x_pos <- dat$x[dat$y == 1] x_neg <- dat$x[dat$y == 0] bench_10000 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean, silent = TRUE), rocr_sensspec(dat$x, dat$y), optimal.cutpoints(X = "x", status = "y", tag.healthy = 0, methods = "Youden", data = dat), proc_sensspec(dat$x, dat$y), thres2(k1 = x_neg, k2 = x_pos, rho = 0.5, method = "empirical", ci = FALSE), times = 100 ) n <- 1e5 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e5 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), times = 100, unit = "ms" ) n <- 1e6 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e6 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), times = 30, unit = "ms" ) n <- 1e7 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e7 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), times = 30, unit = "ms" ) results <- rbind( data.frame(time = summary(bench_100)$median, Solution = summary(bench_100)$expr, n = 100), data.frame(time = summary(bench_1000)$median, Solution = summary(bench_1000)$expr, n = 1000), data.frame(time = summary(bench_10000)$median, Solution = summary(bench_10000)$expr, n = 10000), data.frame(time = summary(bench_1e5)$median, Solution = summary(bench_1e5)$expr, n = 1e5), data.frame(time = summary(bench_1e6)$median, Solution = summary(bench_1e6)$expr, n = 1e6), data.frame(time = summary(bench_1e7)$median, Solution = summary(bench_1e7)$expr, n = 1e7) ) results$Solution <- as.character(results$Solution) results$Solution[grep(pattern = "cutpointr", x = results$Solution)] <- "cutpointr" results$Solution[grep(pattern = "rocr", x = results$Solution)] <- "ROCR" results$Solution[grep(pattern = "optimal", x = results$Solution)] <- "OptimalCutpoints" results$Solution[grep(pattern = "proc", x = results$Solution)] <- "pROC" results$Solution[grep(pattern = "thres", x = results$Solution)] <- "ThresholdROC" results$task <- "Cutpoint Estimation" ``` ```{r, echo = FALSE} # These are the original results on our system # dput(results) results <- structure(list(time = c(4.5018015, 1.812802, 0.662101, 2.2887015, 1.194301, 4.839401, 2.1764015, 0.981001, 45.0568005, 36.2398515, 8.5662515, 5.667101, 2538.612001, 4.031701, 2503.8012505, 45.384501, 43.118751, 37.150151, 465.003201, 607.023851, 583.0950005, 5467.332801, 7850.2587, 7339.356101), Solution = c("cutpointr", "ROCR", "pROC", "OptimalCutpoints", "ThresholdROC", "cutpointr", "ROCR", "pROC", "OptimalCutpoints", "ThresholdROC", "cutpointr", "ROCR", "OptimalCutpoints", "pROC", "ThresholdROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC"), n = c(100, 100, 100, 100, 100, 1000, 1000, 1000, 1000, 1000, 10000, 10000, 10000, 10000, 10000, 1e+05, 1e+05, 1e+05, 1e+06, 1e+06, 1e+06, 1e+07, 1e+07, 1e+07), task = c("Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation")), row.names = c(NA, -24L), class = "data.frame") ``` ```{r, eval = FALSE} # ROCR package rocr_roc <- function(x, class) { pred <- ROCR::prediction(x, class) perf <- ROCR::performance(pred, "sens", "spec") return(NULL) } # pROC package proc_roc <- function(x, class) { r <- pROC::roc(class, x, algorithm = 2, levels = c(0, 1), direction = "<") return(NULL) } ``` ```{r, eval = FALSE, echo = FALSE} n <- 100 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_100 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 100, unit = "ms" ) n <- 1000 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1000 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 100, unit = "ms" ) n <- 10000 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_10000 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 100, unit = "ms" ) n <- 1e5 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e5 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 100, unit = "ms" ) n <- 1e6 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e6 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 30, unit = "ms" ) n <- 1e7 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e7 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 30, unit = "ms" ) results_roc <- rbind( data.frame(time = summary(bench_100)$median, Solution = summary(bench_100)$expr, n = 100), data.frame(time = summary(bench_1000)$median, Solution = summary(bench_1000)$expr, n = 1000), data.frame(time = summary(bench_10000)$median, Solution = summary(bench_10000)$expr, n = 10000), data.frame(time = summary(bench_1e5)$median, Solution = summary(bench_1e5)$expr, n = 1e5), data.frame(time = summary(bench_1e6)$median, Solution = summary(bench_1e6)$expr, n = 1e6), data.frame(time = summary(bench_1e7)$median, Solution = summary(bench_1e7)$expr, n = 1e7) ) results_roc$Solution <- as.character(results_roc$Solution) results_roc$Solution[grep(pattern = "cutpointr", x = results_roc$Solution)] <- "cutpointr" results_roc$Solution[grep(pattern = "rocr", x = results_roc$Solution)] <- "ROCR" results_roc$Solution[grep(pattern = "proc", x = results_roc$Solution)] <- "pROC" results_roc$task <- "ROC curve calculation" ``` ```{r, echo = FALSE} # Our results results_roc <- structure(list(time = c(0.7973505, 1.732651, 0.447701, 0.859301, 2.0358515, 0.694802, 1.878151, 5.662151, 3.6580505, 11.099251, 42.8208515, 35.3293005, 159.8100505, 612.471901, 610.4337005, 2032.693551, 7806.3854515, 7081.897251), Solution = c("cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC"), n = c(100, 100, 100, 1000, 1000, 1000, 10000, 10000, 10000, 1e+05, 1e+05, 1e+05, 1e+06, 1e+06, 1e+06, 1e+07, 1e+07, 1e+07), task = c("ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation")), row.names = c(NA, -18L), class = "data.frame") ``` ```{r, echo = FALSE} library(ggplot2) results_all <- dplyr::bind_rows(results, results_roc) ggplot(results_all, aes(x = n, y = time, col = Solution, shape = Solution)) + geom_point(size = 3) + geom_line() + scale_y_log10(breaks = c(0.5, 1, 2, 3, 5, 10, 25, 100, 250, 1000, 5000, 1e4, 15000)) + scale_x_log10(breaks = c(100, 1000, 1e4, 1e5, 1e6, 1e7)) + ylab("Median Time (milliseconds, log scale)") + xlab("Sample Size (log scale)") + theme_bw() + theme(legend.position = "bottom", legend.key.width = unit(0.8, "cm"), panel.spacing = unit(1, "lines")) + facet_grid(~task) ``` ```{r, echo = FALSE} library(tidyr) res_table <- tidyr::spread(results_all, Solution, time) res_table <- dplyr::arrange(res_table, task) knitr::kable(res_table) ``` cutpointr/vignettes/cutpointr_estimation.Rmd0000644000176200001440000002260214726126006021246 0ustar liggesusers--- title: "Robust cutpoint estimation" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Robust cutpoint estimation} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` # More robust cutpoint estimation methods ## Bootstrapped cutpoints It has been shown that bagging can substantially improve performance of a wide range of types of models in regression as well as in classification tasks. This method is available for cutpoint estimation via the `maximize_boot_metric` and `minimize_boot_metric` functions. If one of these functions is used as `method`, `boot_cut` bootstrap samples are drawn, the cutpoint optimization is carried out in each one and a summary (e.g. the mean) of the resulting optimal cutpoints on the bootstrap samples is returned as the optimal cutpoint in `cutpointr`. Note that if bootstrap validation is run, i.e. if `boot_runs` is larger zero, an outer bootstrap will be executed. In the bootstrap validation routine `boot_runs` bootstrap samples are generated and each one is again bootstrapped `boot_cut` times. This may lead to long run times, so activating the built-in parallelization may be advisable. The advantages of bootstrapping the optimal cutpoint are that the procedure doesn't possess parameters that have to be tuned, unlike the LOESS smoothing, that it doesn't rely on assumptions, unlike the Normal method, and that it is applicable to any metric that can be used with `minimize_metric` or `maximize_metric`, unlike the Kernel method. Furthermore, like Random Forests cannot be overfit by increasing the number of trees, the bootstrapped cutpoints cannot be overfit by running an excessive amount of `boot_cut` repetitions. ```{r, cache=TRUE} library(cutpointr) set.seed(100) cutpointr(suicide, dsi, suicide, gender, method = maximize_boot_metric, boot_cut = 200, summary_func = mean, metric = accuracy, silent = TRUE) ``` ## LOESS smoothing for selecting a cutpoint When using `maximize_metric` and `minimize_metric` the optimal cutpoint is selected by searching the maximum or minimum of the metric function. For example, we may want to minimize the misclassification cost. Since false negatives (a suicide attempt was not anticipated) can be regarded as much more severe than false positives we can set the cost of a false negative `cost_fn` for example to ten times the cost of a false positive. ```{r} opt_cut <- cutpointr(suicide, dsi, suicide, gender, method = minimize_metric, metric = misclassification_cost, cost_fp = 1, cost_fn = 10) ``` ```{r} plot_metric(opt_cut) ``` As this "optimal" cutpoint may depend on minor differences between the possible cutoffs, smoothing of the function of metric values by cutpoint value might be desirable, especially in small samples. The `minimize_loess_metric` and `maximize_loess_metric` functions can be used to smooth the function so that the optimal cutpoint is selected based on the smoothed metric values. Options to modify the smoothing, which is implemented using `loess.as` from the **fANCOVA** package, include: - `criterion`: the criterion for automatic smoothing parameter selection: "aicc" denotes bias-corrected AIC criterion, "gcv" denotes generalized cross-validation. - `degree`: the degree of the local polynomials to be used. It can be 0, 1 or 2. - `family`: if "gaussian" fitting is by least-squares, and if "symmetric" a re-descending M estimator is used with Tukey's biweight function. - `user.span`: the user-defined parameter which controls the degree of smoothing. Using parameters for the LOESS smoothing of `criterion = "aicc"`, `degree = 2`, `family = "symmetric"`, and `user.span = 0.7` we get the following smoothed versions of the above metrics: ```{r, message = FALSE} opt_cut <- cutpointr(suicide, dsi, suicide, gender, method = minimize_loess_metric, criterion = "aicc", family = "symmetric", degree = 2, user.span = 0.7, metric = misclassification_cost, cost_fp = 1, cost_fn = 10) ``` ```{r} plot_metric(opt_cut) ``` The optimal cutpoint for the female subgroup changes to 3. Note, though, that there are no reliable rules for selecting the "best" smoothing parameters. Notably, the LOESS smoothing is sensitive to the number of unique cutpoints. A large number of unique cutpoints generally leads to a more volatile curve of metric values by cutpoint value, even after smoothing. Thus, the curve tends to be undersmoothed in that scenario. The unsmoothed metric values are returned in `opt_cut$roc_curve` in the column `m_unsmoothed`. ## Smoothing via Generalized Additive Models for selecting a cutpoint In a similar fashion, the function of metric values per cutpoint can be smoothed using Generalized Additive Models with smooth terms. Internally, `mgcv::gam` carries out the smoothing which can be customized via the arguments `formula` and `optimizer`, see `help("gam", package = "mgcv")`. Most importantly, the GAM can be specified by altering the default formula, for example the smoothing function could be configured to apply cubic regression splines (`"cr"`) as the smooth term. As the `suicide` data has only very few unique cutpoints, it is not very suitable for showcasing the GAM smoothing, so we will use two classes of the `iris` data here. In this case, the purely empirical method and the GAM smoothing lead to identical cutpoints, but in practice the GAM smoothing tends to be more robust, especially with larger data. An attractive feature of the GAM smoothing is that the default values tend to work quite well and usually require no tuning, eliminating researcher degrees of freedom. ```{r} library(ggplot2) exdat <- iris exdat <- exdat[exdat$Species != "setosa", ] opt_cut <- cutpointr(exdat, Petal.Length, Species, method = minimize_gam_metric, formula = m ~ s(x.sorted, bs = "cr"), metric = abs_d_sens_spec) plot_metric(opt_cut) ``` ### Parametric method assuming normality The Normal method in `oc_youden_normal` is a parametric method for maximizing the Youden-Index or equivalently the sum of $Se$ and $Sp$. It relies on the assumption that the predictor for both the negative and positive observations is normally distributed. In that case it can be shown that $$c^* = \frac{(\mu_P \sigma_N^2 - \mu_N \sigma_P^2) - \sigma_N \sigma_P \sqrt{(\mu_N - \mu_P)^2 + (\sigma_N^2 - \sigma_P^2) log(\sigma_N^2 / \sigma_P^2)}}{\sigma_N^2 - \sigma_P^2}$$ where the negative class is normally distributed with $\sim N(\mu_N, \sigma_N^2)$ and the positive class independently normally distributed with $\sim N(\mu_P, \sigma_P^2)$ provides the optimal cutpoint $c^*$ that maximizes the Youden-Index. If $\sigma_N$ and $\sigma_P$ are equal, the expression can be simplified to $c^* = \frac{\mu_N + \mu_P}{2}$. However, the `oc_youden_normal` method in cutpointr always assumes unequal standard deviations. Since this method does not select a cutpoint from the observed predictor values, it is questionable which values for $Se$ and $Sp$ should be reported. Here, the Youden-Index can be calculated as $$J = \Phi(\frac{c^* - \mu_N}{\sigma_N}) - \Phi(\frac{c^* - \mu_P}{\sigma_P})$$ if the assumption of normality holds. However, since there exist several methods that do not select cutpoints from the available observations and to unify the reporting of metrics for these methods, **cutpointr** reports all metrics, e.g. $Se$ and $Sp$, based on the empirical observations. ```{r} cutpointr(suicide, dsi, suicide, gender, method = oc_youden_normal) ``` ### Nonparametric kernel method A nonparametric alternative is the Kernel method [@fluss_estimation_2005]. Here, the empirical distribution functions are smoothed using the Gaussian kernel functions $\hat{F}_N(t) = \frac{1}{n} \sum^n_{i=1} \Phi(\frac{t - y_i}{h_y})$ and $\hat{G}_P(t) = \frac{1}{m} \sum^m_{i=1} \Phi(\frac{t - x_i}{h_x})$ for the negative and positive classes respectively. Following Silverman's plug-in "rule of thumb" the bandwidths are selected as $h_y = 0.9 * min\{s_y, iqr_y/1.34\} * n^{-0.2}$ and $h_x = 0.9 * min\{s_x, iqr_x/1.34\} * m^{-0.2}$ where $s$ is the sample standard deviation and $iqr$ is the inter quartile range. It has been demonstrated that AUC estimation is rather insensitive to the choice of the bandwidth procedure [@faraggi_estimation_2002] and thus the plug-in bandwidth estimator has also been recommended for cutpoint estimation. The `oc_youden_kernel` function in **cutpointr** uses a Gaussian kernel and the direct plug-in method for selecting the bandwidths. The kernel smoothing is done via the `bkde` function from the **KernSmooth** package [@wand_kernsmooth:_2013]. Again, there is a way to calculate the Youden-Index from the results of this method [@fluss_estimation_2005] which is $$\hat{J} = max_c \{\hat{F}_N(c) - \hat{G}_N(c) \}$$ but as before we prefer to report all metrics based on applying the cutpoint that was estimated using the Kernel method to the empirical observations. ```{r} cutpointr(suicide, dsi, suicide, gender, method = oc_youden_kernel) ```cutpointr/vignettes/cutpointr_bootstrapping.Rmd0000644000176200001440000001725014726066534022001 0ustar liggesusers--- title: "cutpointr: Bootstrapping" author: "Christian Thiele, Lorenz A. Kapsner" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Bootstrapping with cutpointr} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console markdown: wrap: 80 --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) ``` Bootstrapping is implemented in **cutpointr** with two goals: 1. Determine optimal cutpoints with bootstrapping (as an alternative to determining them without bootstrapping) 2. Validate (any) cutpoint optimization with bootstrapping This vignette will briefly go through some examples for both approaches. # Determine optimal cutpoints ## Without bootstrapping: `maximize_metric` As a first basic example, the cutpoint optimization will be demonstrated without any bootstrapping by maximizing the Youden-Index. Using the method `maximize_metric`, this is performed on the full data set: ```{r} library(cutpointr) data(suicide) opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_metric, metric = youden, pos_class = "yes", direction = ">=" ) summary(opt_cut) ``` The fields in the resulting R object `opt_cut` are to be interpreted as follows: - `$optimal_cutpoint`: The optimal cutpoint determined by maximizing the Youden-Index on the full `suicide` dataset. - `$sensitivity`: The sensitivity when applying the cutpoint to the full dataset. - `$specificity`: The specificity when applying the cutpoint to the full dataset. - `$youden`: The maximal Youden-Index (= sensitivity + specificity - 1), determined by the optimization. ## Bootstrap cutpoints: `maximize_boot_metric` The determination of the optimal cutpoint can also be performed using bootstrapping. Therefore, the methods `maximize_boot_metric`/`minimize_boot_metric` need to be chosen. These functions provide further arguments that can be used to configure the bootstrapping. These arguments can be viewed with `help("maximize_boot_metric", "cutpointr")`. The most important arguments are: - `boot_cut`: The number of bootstrapping repetitions. - `boot_stratify`: If the bootstrap samples are drawn in both classes separately before combining them, keep the number of positives/negatives constant in every sample. - `summary_func`: The summary function to aggregate the optimal cutpoints from the bootstrapping to arrive at one final optimal cutpoint. The cutpoint is optimized in n=`boot_cut` bootstrap samples by maximizing/ minimizing the respective metric (e.g., the Youden-index in this example) in each of these bootstrap samples. Finally, the summary function is applied to aggregate the optimal cutpoints from the n=`boot_cut` bootstrap samples into one final 'optimal' cutpoint. ```{r} set.seed(123) opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_boot_metric, boot_cut = 200, summary_func = mean, metric = youden, pos_class = "yes", direction = ">=" ) summary(opt_cut) ``` The fields in the resulting R object `opt_cut` are to be interpreted as follows: - `$optimal_cutpoint`: The optimal cutpoint, which is the aggregated value (as defined with `summary_func`) over all n=`boot_cut` bootstrap samples. Please note that no uncertainty measure (standard deviation, 95%-CI, etc.) is available here (a bootstrap distribution of these cutpoints can be generated using outer bootstrapping with `boot_runs > 0` and `maximize_metric`, as explained below). - `$sensitivity`: The sensitivity when applying the optimal cutpoint to the full dataset. - `$specificity`: The specificity when applying the optimal cutpoint to the full dataset. - `$youden`: The Youden-Index when applying the optimal cutpoint to the full dataset. # Validate cutpoint optimization with bootstrapping Any chosen methods to find the optimal cutpoints can be subsequently validated with bootstrapping. This can easily be activated by setting the argument `boot_runs` \> 0. Please be aware that the first steps to calculate the optimal cutpoints with the specified method (as described above) will be performed in the very same manner as above, resulting in the same outputs as above (depending on the seed when bootstrapping cutpoints). However, the method to calculate the optimal cutpoints will then additionally be performed on n=`boot_runs` bootstrap samples. For each of these bootstrap samples, several metrics and performance measures are available from the resulting `$boot` object, both for the *in-bag* (suffix: '\_b') and the *out-of-bag* (suffix: '\_oob') bootstrap samples. Please note that the optimal cutpoint is determined on the in-bag samples only and then just applied to the out-of-bag samples for validation purposes, so its value is available only once in the `$boot` object without a suffix. ## `maximize_metric` ```{r} opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_metric, metric = youden, pos_class = "yes", direction = ">=", boot_runs = 100 ) ``` The interpretation of fields in the resulting R object `opt_cut` is the same as above. The results from the bootstrapping are available from `$boot`. ```{r} summary(opt_cut) opt_cut$boot[[1]] |> head() ``` ## `maximize_boot_metric` When bootstrapping cutpoints and also using the validation with bootstrapping, the optimal cutpoint will again first be determined as above in n=`boot_cut` bootstrap samples by maximizing/ minimizing the respective metric in each of these bootstrap samples and then by applying the summary function to aggregate the optimal cutpoints from the n=`boot_cut` bootstrap samples into one final 'optimal' cutpoint. Hence, using the same seeds here results in the same outputs as above, where no outer bootstrapping is applied. In the validation routine, the chosen cutpoint optimization is then repeated in each of the n=`boot_runs` (outer) bootstrap samples: the optimal cutpoint is determined in each bootstrap sample by optimizing the `metric` on n=`boot_cut` (inner) bootstrap samples and applying the `summary_func` to aggregate them into one value. Since the (inner) bootstrapping of optimal cutpoints is performed in each of the (outer) validation bootstrap samples, this can be computational very expensive and take some time to finish. Therefore, parallelization is implemented in `cutpointr` by just setting its argument `allowParallel = TRUE` and initializing a parallel environment. ```{r} library(doParallel) library(doRNG) cl <- makeCluster(2) # 2 cores registerDoParallel(cl) registerDoRNG(12) set.seed(123) opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_boot_metric, boot_cut = 200, summary_func = mean, metric = youden, pos_class = "yes", direction = ">=", boot_runs = 100, allowParallel = TRUE ) stopCluster(cl) ``` Again, the interpretation of fields in the resulting R object `opt_cut` is the same as above. The results from the bootstrapping are available from `$boot`. ```{r} summary(opt_cut) opt_cut$boot[[1]] |> head() ``` Some visualizations of the bootstrapping results are available with the `plot` function: ```{r} plot(opt_cut) ``` The two plots in the lower half can be generated separately with `plot_cut_boot(opt_cut)` and `plot_metric_boot(opt_cut)`.cutpointr/vignettes/cutpointr.Rmd0000644000176200001440000006042014726126404017014 0ustar liggesusers--- title: "An introduction to cutpointr" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{An introduction to cutpointr} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` **cutpointr** is an R package for tidy calculation of "optimal" cutpoints. It supports several methods for calculating cutpoints and includes several metrics that can be maximized or minimized by selecting a cutpoint. Some of these methods are designed to be more robust than the simple empirical optimization of a metric. Additionally, **cutpointr** can automatically bootstrap the variability of the optimal cutpoints and return out-of-bag estimates of various performance metrics. ## Installation You can install **cutpointr** from CRAN using the menu in RStudio or simply: ```{r CRAN, eval = FALSE} install.packages("cutpointr") ``` ## Example For example, the optimal cutpoint for the included data set is 2 when maximizing the sum of sensitivity and specificity. ```{r} library(cutpointr) data(suicide) head(suicide) cp <- cutpointr(suicide, dsi, suicide, method = maximize_metric, metric = sum_sens_spec) ``` ```{r} summary(cp) ``` ```{r} plot(cp) ``` When considering the optimality of a cutpoint, we can only make a judgement based on the sample at hand. Thus, the estimated cutpoint may not be optimal within the population or on unseen data, which is why we sometimes put the "optimal" in quotation marks. `cutpointr` makes assumptions about the direction of the dependency between `class` and `x`, if `direction` and / or `pos_class` or `neg_class` are not specified. The same result as above can be achieved by manually defining `direction` and the positive / negative classes which is slightly faster, since the classes and direction don't have to be determined: ```{r} opt_cut <- cutpointr(suicide, dsi, suicide, direction = ">=", pos_class = "yes", neg_class = "no", method = maximize_metric, metric = youden) ``` `opt_cut` is a data frame that returns the input data and the ROC curve (and optionally the bootstrap results) in a nested tibble. Methods for summarizing and plotting the data and results are included (e.g. `summary`, `plot`, `plot_roc`, `plot_metric`) To inspect the optimization, the function of metric values per cutpoint can be plotted using `plot_metric`, if an optimization function was used that returns a metric column in the `roc_curve` column. For example, the `maximize_metric` and `minimize_metric` functions do so: ```{r} plot_metric(opt_cut) ``` Predictions for new data can be made using `predict`: ```{r} predict(opt_cut, newdata = data.frame(dsi = 0:5)) ``` ## Features - Calculation of optimal cutpoints in binary classification tasks - Tidy output, integrates well with functions from the tidyverse - Functions for plotting ROC curves, metric distributions and more - Bootstrapping for simulating the cutpoint variability and for obtaining out-of-bag estimates of various metrics (as a form of internal validation) with optional parallelisation - Multiple methods for calculating cutpoints - Multiple metrics can be chosen for maximization / minimization - Tidyeval # Calculating cutpoints ## Method functions for cutpoint estimation The included methods for calculating cutpoints are: - `maximize_metric`: Maximize the metric function - `minimize_metric`: Minimize the metric function - `maximize_loess_metric`: Maximize the metric function after LOESS smoothing - `minimize_loess_metric`: Minimize the metric function after LOESS smoothing - `maximize_gam_metric`: Maximize the metric function after smoothing via Generalized Additive Models - `minimize_gam_metric`: Minimize the metric function after smoothing via Generalized Additive Models - `maximize_boot_metric`: Bootstrap the optimal cutpoint when maximizing a metric - `minimize_boot_metric`: Bootstrap the optimal cutpoint when minimizing a metric - `oc_manual`: Specify the cutoff value manually - `oc_mean`: Use the sample mean as the "optimal" cutpoint - `oc_median`: Use the sample median as the "optimal" cutpoint - `oc_youden_kernel`: Maximize the Youden-Index after kernel smoothing the distributions of the two classes - `oc_youden_normal`: Maximize the Youden-Index parametrically assuming normally distributed data in both classes ## Metric functions The included metrics to be used with the minimization and maximization methods are: - `accuracy`: Fraction correctly classified - `abs_d_sens_spec`: The absolute difference of sensitivity and specificity - `abs_d_ppv_npv`: The absolute difference between positive predictive value (PPV) and negative predictive value (NPV) - `roc01`: Distance to the point (0,1) on ROC space - `cohens_kappa`: Cohen's Kappa - `sum_sens_spec`: sensitivity + specificity - `sum_ppv_npv`: The sum of positive predictive value (PPV) and negative predictive value (NPV) - `prod_sens_spec`: sensitivity * specificity - `prod_ppv_npv`: The product of positive predictive value (PPV) and negative predictive value (NPV) - `youden`: Youden- or J-Index = sensitivity + specificity - 1 - `odds_ratio`: (Diagnostic) odds ratio - `risk_ratio`: risk ratio (relative risk) - `p_chisquared`: The p-value of a chi-squared test on the confusion matrix - `cost_misclassification`: The sum of the misclassification cost of false positives and false negatives. Additional arguments: cost_fp, cost_fn - `total_utility`: The total utility of true / false positives / negatives. Additional arguments: utility_tp, utility_tn, cost_fp, cost_fn - `F1_score`: The F1-score (2 * TP) / (2 * TP + FP + FN) - `metric_constrain`: Maximize a selected metric given a minimal value of another selected metric - `sens_constrain`: Maximize sensitivity given a minimal value of specificity - `spec_constrain`: Maximize specificity given a minimal value of sensitivity - `acc_constrain`: Maximize accuracy given a minimal value of sensitivity Furthermore, the following functions are included which can be used as metric functions but are more useful for plotting purposes, for example in `plot_cutpointr`, or for defining new metric functions: `tp`, `fp`, `tn`, `fn`, `tpr`, `fpr`, `tnr`, `fnr`, `false_omission_rate`, `false_discovery_rate`, `ppv`, `npv`, `precision`, `recall`, `sensitivity`, and `specificity`. The inputs to the arguments `method` and `metric` are functions so that user-defined functions can easily be supplied instead of the built-in ones. ## Separate subgroups and bootstrapping Cutpoints can be separately estimated on subgroups that are defined by a third variable, `gender` in this case. Additionally, if `boot_runs` is larger zero, `cutpointr` will carry out the usual cutpoint calculation on the full sample, just as before, and additionally on `boot_runs` bootstrap samples. This offers a way of gauging the out-of-sample performance of the cutpoint estimation method. If a subgroup is given, the bootstrapping is carried out separately for every subgroup which is also reflected in the plots and output. ```{r separate subgroups and bootstrapping, eval = FALSE} set.seed(12) opt_cut_b <- cutpointr(suicide, dsi, suicide, boot_runs = 1000) ``` ```{r} opt_cut_b ``` The returned object has the additional column `boot` which is a nested tibble that includes the cutpoints per bootstrap sample along with the metric calculated using the function in `metric` and various default metrics. The metrics are suffixed by `_b` to indicate in-bag results or `_oob` to indicate out-of-bag results: ```{r} opt_cut_b$boot ``` The summary and plots include additional elements that summarize or display the bootstrap results: ```{r} summary(opt_cut_b) plot(opt_cut_b) ``` ### Parallelized bootstrapping Using `foreach` and `doRNG` the bootstrapping can be parallelized easily. The **doRNG** package is being used to make the bootstrap sampling reproducible. ```{r, eval = FALSE} library(doParallel) cl <- makeCluster(2) # 2 cores registerDoParallel(cl) registerDoRNG(12) # Reproducible parallel loops using doRNG opt_cut <- cutpointr(suicide, dsi, suicide, gender, pos_class = "yes", direction = ">=", boot_runs = 1000, allowParallel = TRUE) stopCluster(cl) opt_cut ``` # Additional features ## Finding all cutpoints with acceptable performance By default, most packages only return the "best" cutpoint and disregard other cutpoints with quite similar performance, even if the performance differences are minuscule. **cutpointr** makes this process more explicit via the `tol_metric` argument. For example, if all cutpoints are of interest that achieve at least an accuracy within `0.05` of the optimally achievable accuracy, `tol_metric` can be set to `0.05` and also those cutpoints will be returned. In the case of the `suicide` data and when maximizing the sum of sensitivity and specificity, empirically the cutpoints 2 and 3 lead to quite similar performances. If `tol_metric` is set to `0.05`, both will be returned. ```{r} library(tidyr) library(dplyr) opt_cut <- cutpointr(suicide, dsi, suicide, metric = sum_sens_spec, tol_metric = 0.05, break_ties = c) opt_cut |> select(optimal_cutpoint, sum_sens_spec) |> unnest(cols = c(optimal_cutpoint, sum_sens_spec)) ``` ## Manual and mean / median cutpoints Using the `oc_manual` function the optimal cutpoint will not be determined based on, for example, a metric but is instead set manually using the `cutpoint` argument. This is useful for supplying and evaluating cutpoints that were found in the literature or in other external sources. The `oc_manual` function could also be used to set the cutpoint to the sample mean using `cutpoint = mean(data$x)`. However, this may introduce bias into the bootstrap validation procedure, since the actual mean of the population is not known and thus the mean to be used as the cutpoint should be automatically determined in every resample. To do so, the `oc_mean` and `oc_median` functions can be used. ```{r, eval = FALSE} set.seed(100) opt_cut_manual <- cutpointr(suicide, dsi, suicide, method = oc_manual, cutpoint = mean(suicide$dsi), boot_runs = 1000) set.seed(100) opt_cut_mean <- cutpointr(suicide, dsi, suicide, method = oc_mean, boot_runs = 1000) ``` ## Nonstandard evaluation via tidyeval The arguments to `cutpointr` do not need to be enclosed in quotes. This is possible thanks to nonstandard evaluation of the arguments, which are evaluated on `data`. Functions that use nonstandard evaluation are often not suitable for programming with. The use of nonstandard evaluation may lead to scoping problems and subsequent obvious as well as possibly subtle errors. **cutpointr** uses tidyeval internally and accordingly the same rules as for programming with `dplyr` apply. Arguments can be unquoted with `!!`: ```{r, eval = FALSE} myvar <- "dsi" cutpointr(suicide, !!myvar, suicide) ``` ## Midpoints So far - which is the default in `cutpointr` - we have considered all unique values of the predictor as possible cutpoints. An alternative could be to use a sequence of equidistant values instead, for example in the case of the `suicide` data all integers in $[0, 10]$. However, with very sparse data and small intervals between the candidate cutpoints (i.e. a 'dense' sequence like `seq(0, 10, by = 0.01)`) this leads to the uninformative evaluation of large ranges of cutpoints that all result in the same metric value. A more elegant alternative, not only for the case of sparse data, that is supported by **cutpointr** is the use of a mean value of the optimal cutpoint and the next highest (if `direction = ">="`) or the next lowest (if `direction = "<="`) predictor value in the data. The result is an optimal cutpoint that is equal to the cutpoint that would be obtained using an infinitely dense sequence of candidate cutpoints and is thus usually more efficient computationally. This behavior can be activated by setting `use_midpoints = TRUE`, which is the default. If we use this setting, we obtain an optimal cutpoint of 1.5 for the complete sample on the `suicide` data instead of 2 when maximizing the sum of sensitivity and specificity. Assume the following small data set: ```{r} dat <- data.frame(outcome = c("neg", "neg", "neg", "pos", "pos", "pos", "pos"), pred = c(1, 2, 3, 8, 11, 11, 12)) ``` Since the distance of the optimal cutpoint (8) to the next lowest observation (3) is rather large we arrive at a range of possible cutpoints that all maximize the metric. In the case of this kind of sparseness it might for example be desirable to classify a new observation with a predictor value of 4 as belonging to the negative class. If `use_midpoints` is set to `TRUE`, the mean of the optimal cutpoint and the next lowest observation is returned as the optimal cutpoint, if direction is `>=`. The mean of the optimal cutpoint and the next highest observation is returned as the optimal cutpoint, if `direction = "<="`. ```{r} opt_cut <- cutpointr(dat, x = pred, class = outcome, use_midpoints = TRUE) plot_x(opt_cut) ``` A simulation demonstrates more clearly that setting `use_midpoints = TRUE` avoids biasing the cutpoints. To simulate the bias of the metric functions, the predictor values of both classes were drawn from normal distributions with constant standard deviations of 10, a constant mean of the negative class of 100 and higher mean values of the positive class that are selected in such a way that optimal Youden-Index values of 0.2, 0.4, 0.6, and 0.8 result in the population. Samples of 9 different sizes were drawn and the cutpoints that maximize the Youden-Index were estimated. The simulation was repeated 10000 times. As can be seen by the mean error, `use_midpoints = TRUE` eliminates the bias that is introduced by otherwise selecting the value of an observation as the optimal cutpoint. If `direction = ">="`, as in this case, the observation that represents the optimal cutpoint is the highest possible cutpoint that leads to the optimal metric value and thus the biases are positive. The methods `oc_youden_normal` and `oc_youden_kernel` are always unbiased, as they don't select a cutpoint based on the ROC-curve or the function of metric values per cutpoint. ```{r, echo = FALSE} plotdat_nomidpoints <- structure(list(sim_nr = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 36L), method = structure(c(1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L), levels = c("emp", "normal", "loess", "boot", "spline", "spline_20", "kernel", "gam"), class = "factor"), n = c(30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 750, 750, 1000, 1000, 1000, 1000, 1000, 1000, 30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 750, 750, 1000, 1000, 1000, 1000, 1000, 1000, 30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 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-0.0220885572014814, 0.243370611433649, 0.209652330609093, -0.00502865663759991, 0.2172246261346, 0.356540958804122, 0.00365942442127361, 0.176594681455494, 0.126956927057327, -0.00270525933073803, 0.120116234221594, 0.210827536708082, 0.00238043252144371, 0.113027315928011, 0.0598624378953727, -0.00538838415690431, 0.0568400730102315, 0.0978115258288965, -0.00165813015281622, 0.0521772135812508, 0.0530224090961669, -0.000416993405198653, 0.0353236911458531, 0.0605493601241619, -0.00446984887315054, 0.0328807595695966, 0.0396438546423947, -0.00331466369719113, 0.0379029847219126, 0.0572435100638761, 0.00220241478536399, 0.0307132312422208), youden = c(0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), row.names = c(NA, -216L), groups = structure(list( sim_nr = 1:36, .rows = structure(list(1:6, 7:12, 13:18, 19:24, 25:30, 31:36, 37:42, 43:48, 49:54, 55:60, 61:66, 67:72, 73:78, 79:84, 85:90, 91:96, 97:102, 103:108, 109:114, 115:120, 121:126, 127:132, 133:138, 139:144, 145:150, 151:156, 157:162, 163:168, 169:174, 175:180, 181:186, 187:192, 193:198, 199:204, 205:210, 211:216), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", "list"))), row.names = c(NA, -36L), .drop = TRUE, class = c("tbl_df", "tbl", "data.frame"))) ``` ```{r, echo = FALSE} library(ggplot2) ggplot(plotdat_nomidpoints, aes(x = n, y = mean_err, color = method, shape = method)) + geom_line() + geom_point() + facet_wrap(~ youden, scales = "fixed") + scale_shape_manual(values = 1:nlevels(plotdat_nomidpoints$method)) + scale_x_log10(breaks = c(30, 50, 75, 100, 150, 250, 500, 750, 1000)) + ggtitle("Bias of all methods when use_midpoints = FALSE", "normally distributed data, 10000 repetitions of simulation") ``` cutpointr/vignettes/vignettedata/0000755000176200001440000000000014726066534017005 5ustar liggesuserscutpointr/vignettes/vignettedata/vignettedata.Rdata0000644000176200001440000476006014726066534022456 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F,ѯoAX3\I6  M uXo)p"ζs e;)hERdV:7l9 `XJ P"ݍGimjadK(ǰ\R %W |Ub7@"EF9(EVR@_ X5 Б 1L,`"{Cg3ssbK}r]~;,cs>\8p5߼):+$ qe Ãjih(7is7Z9D3chiN EITlX#R[Mfj4k1PlhXk QFX5DjLiD0F4L0̨$Gb;4Gg@_;;gM(Pm_NO ^ېYJ-VA-*Al6 n=i>?gtT׋#jtQ% J%WOW._q(f]w]BA3cutpointr/src/0000755000176200001440000000000014726136577013112 5ustar liggesuserscutpointr/src/RcppExports.cpp0000644000176200001440000001044614726125562016104 0ustar liggesusers// Generated by using Rcpp::compileAttributes() -> do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include using namespace Rcpp; #ifdef RCPP_USE_GLOBAL_ROSTREAM Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); #endif // any_inf bool any_inf(NumericVector x); RcppExport SEXP _cutpointr_any_inf(SEXP xSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); rcpp_result_gen = Rcpp::wrap(any_inf(x)); return rcpp_result_gen; END_RCPP } // get_rev_dups LogicalVector get_rev_dups(NumericVector x); RcppExport SEXP _cutpointr_get_rev_dups(SEXP xSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); rcpp_result_gen = Rcpp::wrap(get_rev_dups(x)); return rcpp_result_gen; END_RCPP } // is_equal_cpp_char LogicalVector is_equal_cpp_char(CharacterVector x, String y); RcppExport SEXP _cutpointr_is_equal_cpp_char(SEXP xSEXP, SEXP ySEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< CharacterVector >::type x(xSEXP); Rcpp::traits::input_parameter< String >::type y(ySEXP); rcpp_result_gen = Rcpp::wrap(is_equal_cpp_char(x, y)); return rcpp_result_gen; END_RCPP } // is_equal_cpp_num LogicalVector is_equal_cpp_num(NumericVector x, double y); RcppExport SEXP _cutpointr_is_equal_cpp_num(SEXP xSEXP, SEXP ySEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< double >::type y(ySEXP); rcpp_result_gen = Rcpp::wrap(is_equal_cpp_num(x, y)); return rcpp_result_gen; END_RCPP } // one_unique_num LogicalVector one_unique_num(NumericVector x); RcppExport SEXP _cutpointr_one_unique_num(SEXP xSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); rcpp_result_gen = Rcpp::wrap(one_unique_num(x)); return rcpp_result_gen; END_RCPP } // one_unique_char LogicalVector one_unique_char(CharacterVector x); RcppExport SEXP _cutpointr_one_unique_char(SEXP xSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< CharacterVector >::type x(xSEXP); rcpp_result_gen = Rcpp::wrap(one_unique_char(x)); return rcpp_result_gen; END_RCPP } // which_are_num IntegerVector which_are_num(NumericVector x, double a); RcppExport SEXP _cutpointr_which_are_num(SEXP xSEXP, SEXP aSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< double >::type a(aSEXP); rcpp_result_gen = Rcpp::wrap(which_are_num(x, a)); return rcpp_result_gen; END_RCPP } // which_are_char IntegerVector which_are_char(CharacterVector x, String a); RcppExport SEXP _cutpointr_which_are_char(SEXP xSEXP, SEXP aSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< CharacterVector >::type x(xSEXP); Rcpp::traits::input_parameter< String >::type a(aSEXP); rcpp_result_gen = Rcpp::wrap(which_are_char(x, a)); return rcpp_result_gen; END_RCPP } static const R_CallMethodDef CallEntries[] = { {"_cutpointr_any_inf", (DL_FUNC) &_cutpointr_any_inf, 1}, {"_cutpointr_get_rev_dups", (DL_FUNC) &_cutpointr_get_rev_dups, 1}, {"_cutpointr_is_equal_cpp_char", (DL_FUNC) &_cutpointr_is_equal_cpp_char, 2}, {"_cutpointr_is_equal_cpp_num", (DL_FUNC) &_cutpointr_is_equal_cpp_num, 2}, {"_cutpointr_one_unique_num", (DL_FUNC) &_cutpointr_one_unique_num, 1}, {"_cutpointr_one_unique_char", (DL_FUNC) &_cutpointr_one_unique_char, 1}, {"_cutpointr_which_are_num", (DL_FUNC) &_cutpointr_which_are_num, 2}, {"_cutpointr_which_are_char", (DL_FUNC) &_cutpointr_which_are_char, 2}, {NULL, NULL, 0} }; RcppExport void R_init_cutpointr(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } cutpointr/src/which.cpp0000644000176200001440000000103514726066534014712 0ustar liggesusers#include using namespace Rcpp; // [[Rcpp::export]] IntegerVector which_are_num(NumericVector x, double a) { int nx = x.size(); std::vector y; y.reserve(nx); for(int i = 0; i < nx; i++) { if (x[i] == a) y.push_back(i+1); } return wrap(y); } // [[Rcpp::export]] IntegerVector which_are_char(CharacterVector x, String a) { int nx = x.size(); std::vector y; y.reserve(nx); for(int i = 0; i < nx; i++) { if (x[i] == a) y.push_back(i+1); } return wrap(y); } cutpointr/src/is_equal_cpp.cpp0000644000176200001440000000070114726066534016253 0ustar liggesusers#include using namespace Rcpp; // [[Rcpp::export]] LogicalVector is_equal_cpp_char(CharacterVector x, String y){ Rcpp::LogicalVector r(x.size()); for(int i=0; i using namespace Rcpp; // [[Rcpp::export]] LogicalVector one_unique_num(NumericVector x) { int n = x.size(); double first_val = x[0]; for(int i = 1; i < n; ++i) { if (x[i] != first_val) return false; } return true; } // [[Rcpp::export]] LogicalVector one_unique_char(CharacterVector x) { int n = x.size(); String first_val = x[0]; for(int i = 1; i < n; ++i) { if (x[i] != first_val) return false; } return true; } cutpointr/src/any_inf.cpp0000644000176200001440000000033614726066534015236 0ustar liggesusers#include using namespace Rcpp; // [[Rcpp::export]] bool any_inf(NumericVector x) { int len = x.size(); for(int i=0; i using namespace Rcpp; // [[Rcpp::export]] LogicalVector get_rev_dups(NumericVector x) { return rev(duplicated(rev(x))); } cutpointr/NAMESPACE0000644000176200001440000000450014726125615013530 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(add_metric,cutpointr) S3method(add_metric,multi_cutpointr) S3method(add_metric,roc_cutpointr) S3method(auc,cutpointr) S3method(auc,roc_cutpointr) S3method(cutpointr,default) S3method(cutpointr,numeric) S3method(plot,cutpointr) S3method(plot,multi_cutpointr) S3method(plot,roc_cutpointr) S3method(plot_roc,cutpointr) S3method(plot_roc,roc_cutpointr) S3method(predict,cutpointr) S3method(print,cutpointr) S3method(print,multi_cutpointr) S3method(print,summary_cutpointr) S3method(print,summary_multi_cutpointr) S3method(summary,cutpointr) S3method(summary,multi_cutpointr) export(F1_score) export(Jaccard) export(abs_d_ppv_npv) export(abs_d_sens_spec) export(acc_constrain) export(accuracy) export(add_metric) export(auc) export(boot_ci) export(boot_test) export(cohens_kappa) export(cutpoint) export(cutpointr) export(cutpointr_) export(cutpoints) export(false_discovery_rate) export(false_omission_rate) export(fn) export(fnr) export(fp) export(fpr) export(maximize_boot_metric) export(maximize_gam_metric) export(maximize_loess_metric) export(maximize_metric) export(metric_constrain) export(minimize_boot_metric) export(minimize_gam_metric) export(minimize_loess_metric) export(minimize_metric) export(misclassification_cost) export(multi_cutpointr) export(nlr) export(npv) export(oc_manual) export(oc_mean) export(oc_median) export(oc_youden_kernel) export(oc_youden_normal) export(odds_ratio) export(p_chisquared) export(plot_cut_boot) export(plot_cutpointr) export(plot_metric) export(plot_metric_boot) export(plot_precision_recall) export(plot_roc) export(plot_sensitivity_specificity) export(plot_x) export(plr) export(ppv) export(precision) export(prod_ppv_npv) export(prod_sens_spec) export(recall) export(risk_ratio) export(roc) export(roc01) export(sens_constrain) export(sensitivity) export(spec_constrain) export(specificity) export(sum_ppv_npv) export(sum_sens_spec) export(tn) export(tnr) export(total_utility) export(tp) export(tpr) export(youden) importFrom(Rcpp,sourceCpp) importFrom(dplyr,"%>%") importFrom(foreach,"%do%") importFrom(purrr,"%>%") importFrom(rlang,"!!") importFrom(rlang,":=") importFrom(rlang,.data) importFrom(stats,median) importFrom(tidyselect,any_of) useDynLib(cutpointr) cutpointr/NEWS.md0000644000176200001440000003143014726127275013415 0ustar liggesusers# cutpointr 1.2.0 ## Changes - Remove dependency pkgmaker - Remove maximize_spline_metric and minimize_spline_metric - Add boot_digits parameter to cutpointr:::print.summary_cutpointr # cutpointr 1.1.2 ## Changes - The (very long) vignette has been split up into multiple vignettes, each with its own topic. @kapsner has written an additional vignette on bootstrapping. - Update ?multi_cutpointr to say that it uses cutpointr(), not cutpointr_() ## Fixes - When using an extra parameter for the metric, such as in sens_constrain, and if bootstrapping was run, that parameter was not forwarded to the bootstrapping # cutpointr 1.1.1 ## Changes - Clarified the help for the boot_stratify parameter - Better examples for the constrained metrics - Added reference and citation for the article about cutpointr in JSS # cutpointr 1.1.0 ## Changes - `boot_ci` now works with multiple cutpoints (multiple cutpoints are possible if `break_ties = c`). - `add_metric` now adds the selected metrics to the bootstrap results, too. - Include metrics that were added with `add_metric` in `summary()`. - Change default value of `subgroup` in `multi_cutpointr` to `NULL` (instead of missing) to make it consistent with `cutpointr`. - No rounding anymore within the internal function `summary_sd` so that the various summary functions now return all values without rounding. - Descriptive statistics in summary functions are now stored with the additional first column "Data" instead of giving the class values as row names. - Nicer printing of summary objects in Rmd documents. - `boot_stratify` is now passed to the method functions so that the bootstrap within `maximize_boot_metric` and `minimize_boot_metric` can be stratified, too. - Subtitles (such as "by class") have been removed from the plots because the subtitles should have read "by subgroup" and because this is already clear from the legend. ## Fixes - Fix a bug in `multi_cutpointr` that forced the `class` variable to be named "suicide". # cutpointr 1.0.32 - Reduce size of tarball for CRAN by removing some superfluous files # cutpointr 1.0.31 - Accelerate tests and vignette building to lower runtime on CRAN # cutpointr 1.0.3 - Some changes for vctrs 0.3.0 and dplyr 1.0.0: Unname and correctly assign classes to a few objects instead of using vctrs::df_cast and vctrs::df_ptype2 to keep compatibility with vctrs 0.2.4 - Added ORCID and article reference to Description # cutpointr 1.0.2 - Minor internal changes for compatibility with the latest updates of some packages from the tidyverse (e.g. `tibble` 3.0.0) # cutpointr 1.0.1 - Prepare for matrix inheriting from class "array" in R 4.0.0 by making a minor change in non-exported utility function `sanitize_metric` # cutpointr 1.0.0 ## Most important changes - `cutpointr` and `roc` now both use tidyeval. `!!` can be used when an argument should be unquoted, as in `dplyr`, e.g. `myvar <- "dsi"; cutpointr(suicide, !!myvar, suicide)`. `cutpointr_` is now deprecated. Transforming variables directly in the call is thus no longer supported, e.g. `cutpointr(suicide, dsi * 2, suicide)` now throws an error. - The object returned by `multi_cutpointr` does not have the `cutpointr` class anymore. ## New functions - A new `boot_ci` function is available that calculates confidence intervals (the empirical quantiles) based on the bootstrap results. - The `auc` function is now exported and can be used to calculate the AUC from a `cutpointr` or `roc_cutpointr` object, e.g. `auc(roc(suicide, dsi, suicide, "yes", "no"))` - `boot_test` is a new function for carrying out a bootstrap test for equivalence of a metric, e.g. the AUC, the Youden-Index or also the optimal cutpoint. The standard deviation is calculated as `sd` of the differences in metric values per bootstrap repetition, then a z-test is calculated. ## Plotting - New `type` argument to `plot_roc` for choosing line or step - The resulting object from `roc_cutpointr` can now simply be plotted with `plot()` ## Bootstrapping - The bootstrapping no longer tries to redraw bootstrap samples if only one class is drawn. In that case the repetition is removed from the results via `.errorhandling = "remove"` in `foreach`. - Subsequently report the number of missing values in the bootstrap results. `summary.cutpointr` and `summary.multi_cutpointr` now print an additional `NAs` column in the bootstrap summary and `cutpointr` issues a message if any bootstrap repeats failed (e.g. because only one class was drawn). - Stratified bootstrapping is now supported via the `boot_stratify` argument. ## Misc - Make the printed output of `summary.cutpointr` and `summary.multi_cutpointr` more compact - No rounding of numbers in `summary.cutpointr` and `summary.multi_cutpointr` any more. The rounding is now done in `print.summary_cutpointr` and `print.summary_multi_cutpointr`, respectively, and can be controlled via the `digits` argument - `plot_metric` has a new `add_unsmoothed` argument for adding the unsmoothed metric values to the plot as a dashed line (default `TRUE`). Helpful to inspect the smoothing of functions like `maximize_gam_metric`. - Add some mathematical details to `?oc_youden_kernel`. - The Readme and vignette have been updated and condensed a bit. - A warning is issued if in `metric_constrain` or one of the other constrained metrics `min_constrain` can not be achieved. ## Fixes - Fix the default for `break_ties` in `cutpointr.default` by setting it to `median` as it was already in `cutpointr.numeric` and `cutpointr_`. # cutpointr 0.7.6 - Let `roc()` return a tibble instead of a data.frame - Printing results of `roc()` is now possible with `plot_roc()` - Extra metric columns can now be added to a `roc_cutpointr` object with `add_metric()` - Add prostate_nodal data set of nodal involvement and acid phosphatase levels in 53 prostate cancer patients - Fix fetching of method name if method was called using `::` or `:::` - Make test of summary printing more tolerant after problems with `tidyr` 0.8.3 - Issue an error if plot is used on a `multi_cutpointr` object - Add a summary method for `multi_cutpointr`, a corresponding `summary_multi_cutpointr` class and a printing method for that class - The column `variable` is not returned anymore by `multi_cutpointr`, because it is identical to `predictor` - Run `multi_cutpointr` only on all numeric columns, if `x = NULL` # cutpointr 0.7.5 - Add constrainable metrics, e.g. sens_constrain to calculate sensitivity given a minimum value for specificity - Fix a bug where dot-arguments were not passed to the metric function in cutpointr_internal - Add a check to ensure that the metric function does not return only missing values - Replace (fix) ">" by ">=" in the documentation of `cutpointr()`. # cutpointr 0.7.4 - Add `sigfig` argument to `print.cutpointr` to allow for specifying the number of significant digits to be printed - Add `add_metric()` function to add further metrics to the output of `cutpointr()` - Add `roc01` metric function to calculate the distance of points on the ROC curve to the point (0,1) on ROC space - Fix `plot_sensitivity_specificity()` if `boot_runs = 0` # cutpointr 0.7.3 - Fix display of bootstrap results in summary - Update benchmarks in Readme # cutpointr 0.7.2 - More tests and modified two tests that led to errors when CRAN checked the package (`spar = NULL` in `maximize_spline_metric`) - Add links to the documentation of all method, plotting, main and metric functions to all other functions of the same family - Add some checks to predict.cutpointr to prevent improper `cutpoint_nr` - The `boot` column is now always returned and `NA`, if no bootstrapping was run, so that the number of returned columns is constant # cutpointr 0.7.1 - Fix a bug in check_method_cols that occurred when a user-supplied method function returned a metric column but no roc_curve - Add chapters to the Readme about GAM, kernel and normal methods - Fix bug in bootstrapping that allowed resamples with only one class to be sampled - `use_midpoints` is now also passed to `method` by `cutpointr` to allow for the calculation of midpoints within `maximize_boot_metric` and `minimize_boot_metric`, which before happened in `cutpointr`, leading to slightly biased cutpoints in certain scenarios # cutpointr 0.7.0 - Change defaults in spline smoothing: `nknots` is now calculated by `stats::.nknots_smspl` and `spar = 1` - Add `cutpoint_tol` argument to define a tolerance around the optimized metric, so that multiple cutpoints in the vicinity of the target metric can be returned and to avoid not returning other "optimal" cutpoints due to floating-point problems - Change default value to `break_ties = c` - If multiple optimal cutpoints are found and they are summarized using `break_ties`, the returned main metric is now not the optimal one but the one corresponding to the summarized cutpoint (thus may be worse than the optimal one) - Add `maximize_gam_metric` and `minimize_gam_metric` for smoothing via generalized additive models - All plots with `geom_ribbon` now use `size = 0` to plot no lines around the (transparent) areas # cutpointr 0.6.0 - Return multiple optimal cutpoints - Add spline smoothing method # cutpointr 0.5.2 - Reformat docs - Remove superfluous check in `plot_cutpointr` - Add metrics `plr` (positive likelihood ratio), `nlr` (negative likelihood ratio), `false_discovery_rate`, and `false_omission_rate` # cutpointr 0.5.1 - In preparation for a new version of tibble that will restore the old printing behaviour and in order to pass the CRAN check the print method for cutpointr has been altered depending on the loaded version of tibble. - `silent` argument for roc(). - Reformat docs to include better formatted equations - Remove superfluous check in plot_cutpointr # cutpointr 0.5.0 - `cutpointr_` now accepts functions instead of character strings as `method` or `metric` - Fix naming the metric in the bootstrap if the supplied function returns an unnamed vector - Fix help for the `use_midpoints` parameter. If TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next lowest observation (for `direction = ">="`) or the next highest observation (for `direction = "<="`) - Rename `sum_ppvnpv`, `prod_ppvnpv`, and `abs_d_ppvnpv` to `sum_ppv_npv`, `prod_ppv_npv`, and `abs_d_ppvnpv` to match the naming scheme to the names of the metrics that optimize sensitivity and specificity - Also all metric names that are returned by the metric functions are now lower case - The `summary_sd` function now also returns 5% and 95% percentiles that are included in the output of `summary` - The default number of bootstrap repeats in `minimize_boot_metric` and `maximize_boot_metric` was changed from 200 to 50 - The message "Running bootstrap..." is now displayed before executing the bootstrap so that possible messages or warnings can be attributed to the main cutpoint estimation or cutpoint estimation during the bootstrapping - The `summary` function now returns a data.frame instead of a list, also the printing method for `summary_cutpointr` has been slightly modified - Add `plot_sensitivity_specificity` for plotting cutpoints vs. sensitivity and specificity on the y-axis - Remove `oc_optimalCutpoints` function - Remove dependencies `ROCR` and `OptimalCutpoints` by rewriting tests and storing benchmark results # cutpointr 0.4.0 - cutpointr() now also works on vectors of raw data, that is without the `data` argument. Thus, it can be used as before by specifying `data`, `x`, and `class` or alternatively without specifying `data` and directly supplying the vectors of predictions and outcomes as `x` and `class`. - `silent` argument for optionally suppressing messages (e.g. which class is assumed to be the positive one) - Parts of the code were rewritten using Rcpp which leads to roughly a threefold speedup with larger n (n > 1e5) - The automatic determination of the positive / negative classes and direction now uses the median of the predictor values per class instead of the mean so that the AUC is always >= 0.5 - The bootstrapping now also returns the in-bag main metric, the out-of-bag ROC curve, and the out-of-bag AUC # cutpointr 0.3.2 - Add multi_cutpointr for running cutpointr_ on multiple predictor variables - Add metric risk_ratio - Use geom_bar instead of geom_histogram if all cutpoints are integer in plot_x and plot_cut_boot or if there is just one unique value - If bootstrapping is run, a bootstrapped confidence interval can be displayed in plot_metric - Add methods for median and mean as cutpoint - Fix display of cutpoint in ROC curve (use get_opt_ind in plot.cutpoint and plot_roc) - Fix picking correct metric in plot_metric_bootcutpointr/inst/0000755000176200001440000000000014726136577013300 5ustar liggesuserscutpointr/inst/CITATION0000644000176200001440000000121414726066534014426 0ustar liggesusersbibentry(bibtype = "Article", title = "{cutpointr}: Improved Estimation and Validation of Optimal Cutpoints in {R}", author = c(person(given = "Christian", family = "Thiele", email = "christian.thiele@fh-bielefeld.de"), person(given = "Gerrit", family = "Hirschfeld")), journal = "Journal of Statistical Software", year = "2021", volume = "98", number = "11", pages = "1--27", doi = "10.18637/jss.v098.i11", header = "To cite cutpointr in publications use:" ) cutpointr/inst/doc/0000755000176200001440000000000014726136577014045 5ustar liggesuserscutpointr/inst/doc/cutpointr_estimation.R0000644000176200001440000000406514726136547020455 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ## ----cache=TRUE--------------------------------------------------------------- library(cutpointr) set.seed(100) cutpointr(suicide, dsi, suicide, gender, method = maximize_boot_metric, boot_cut = 200, summary_func = mean, metric = accuracy, silent = TRUE) ## ----------------------------------------------------------------------------- opt_cut <- cutpointr(suicide, dsi, suicide, gender, method = minimize_metric, metric = misclassification_cost, cost_fp = 1, cost_fn = 10) ## ----------------------------------------------------------------------------- plot_metric(opt_cut) ## ----message = FALSE---------------------------------------------------------- opt_cut <- cutpointr(suicide, dsi, suicide, gender, method = minimize_loess_metric, criterion = "aicc", family = "symmetric", degree = 2, user.span = 0.7, metric = misclassification_cost, cost_fp = 1, cost_fn = 10) ## ----------------------------------------------------------------------------- plot_metric(opt_cut) ## ----------------------------------------------------------------------------- library(ggplot2) exdat <- iris exdat <- exdat[exdat$Species != "setosa", ] opt_cut <- cutpointr(exdat, Petal.Length, Species, method = minimize_gam_metric, formula = m ~ s(x.sorted, bs = "cr"), metric = abs_d_sens_spec) plot_metric(opt_cut) ## ----------------------------------------------------------------------------- cutpointr(suicide, dsi, suicide, gender, method = oc_youden_normal) ## ----------------------------------------------------------------------------- cutpointr(suicide, dsi, suicide, gender, method = oc_youden_kernel) cutpointr/inst/doc/cutpointr_plotting.Rmd0000644000176200001440000001052614726066534020457 0ustar liggesusers--- title: "Plotting with cutpointr" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Plotting with cutpointr} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` **cutpointr** includes several convenience functions for plotting data from a `cutpointr` object. These include: - `plot_cutpointr`: General purpose plotting function for cutpointr or roc_cutpointr objects - `plot_cut_boot`: Plot the bootstrapped distribution of optimal cutpoints - `plot_metric`: If `maximize_metric` or `minimize_metric` was used this function plots all possible cutoffs on the x-axis vs. the respective metric values on the y-axis. If bootstrapping was run, a confidence interval based on the bootstrapped distribution of metric values at each cutpoint can be displayed. To display no confidence interval set `conf_lvl = 0`. - `plot_metric_boot`: Plot the distribution of out-of-bag metric values - `plot_precision_recall`: Plot the precision recall curve - `plot_sensitivity_specificity`: Plot all cutpoints vs. sensitivity and specificity - `plot_roc`: Plot the ROC curve - `plot_x`: Plot the distribution of the predictor variable ```{r, fig.width=4, fig.height=3} library(cutpointr) set.seed(123) opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500) plot_cut_boot(opt_cut_b_g) plot_metric(opt_cut_b_g, conf_lvl = 0.9) plot_metric_boot(opt_cut_b_g) plot_precision_recall(opt_cut_b_g) plot_sensitivity_specificity(opt_cut_b_g) plot_roc(opt_cut_b_g) ``` All plot functions, except for the standard plot method that returns a composed plot, return `ggplot` objects than can be further modified. For example, changing labels, title, and the theme can be achieved this way: ```{r, fig.width=4, fig.height=3} library(ggplot2) p <- plot_x(opt_cut_b_g) p + ggtitle("Distribution of dsi") + theme_minimal() + xlab("Depression score") ``` ## Flexible plotting function Using `plot_cutpointr` any metric can be chosen to be plotted on the x- or y-axis and results of `cutpointr()` as well as `roc()` can be plotted. If a `cutpointr` object is to be plotted, it is thus irrelevant which `metric` function was chosen for cutpoint estimation. Any metric that can be calculated based on the ROC curve can be subsequently plotted as only the true / false positives / negatives over all cutpoints are needed. That way, not only the above plots can be produced, but also any combination of two metrics (or metric functions) and / or cutpoints. The built-in metric functions as well as user-defined functions or anonymous functions can be supplied to `xvar` and `yvar`. If bootstrapping was run, confidence intervals can be plotted around the y-variable. This is especially useful if the cutpoints, available in the `cutpoints` function, are placed on the x-axis. Note that confidence intervals can only be correctly plotted if the values of `xvar` are constant across bootstrap samples. For example, confidence intervals for TPR by FPR (a ROC curve) cannot be plotted easily, as the values of the false positive rate vary per bootstrap sample. ```{r, fig.width=4, fig.height=3, cache=FALSE} set.seed(1234) opt_cut_b <- cutpointr(suicide, dsi, suicide, boot_runs = 500) plot_cutpointr(opt_cut_b, xvar = cutpoints, yvar = sum_sens_spec, conf_lvl = 0.9) plot_cutpointr(opt_cut_b, xvar = fpr, yvar = tpr, aspect_ratio = 1, conf_lvl = 0) plot_cutpointr(opt_cut_b, xvar = cutpoint, yvar = tp, conf_lvl = 0.9) + geom_point() ``` ## Manual plotting Since `cutpointr` returns a `data.frame` with the original data, bootstrap results, and the ROC curve in nested tibbles, these data can be conveniently extracted and plotted manually. The relevant nested tibbles are in the columns `data`, `roc_curve` and `boot`. The following is an example of accessing and plotting the grouped data. ```{r, fig.width=4, fig.height=3} library(dplyr) library(tidyr) opt_cut_b_g |> select(data, subgroup) |> unnest(cols = data) |> ggplot(aes(x = suicide, y = dsi)) + geom_boxplot(alpha = 0.3) + facet_grid(~subgroup) ```cutpointr/inst/doc/cutpointr_estimation.html0000644000176200001440000017013214726136547021217 0ustar liggesusers Robust cutpoint estimation

Robust cutpoint estimation

Christian Thiele

2024-12-10

More robust cutpoint estimation methods

Bootstrapped cutpoints

It has been shown that bagging can substantially improve performance of a wide range of types of models in regression as well as in classification tasks. This method is available for cutpoint estimation via the maximize_boot_metric and minimize_boot_metric functions. If one of these functions is used as method, boot_cut bootstrap samples are drawn, the cutpoint optimization is carried out in each one and a summary (e.g. the mean) of the resulting optimal cutpoints on the bootstrap samples is returned as the optimal cutpoint in cutpointr. Note that if bootstrap validation is run, i.e. if boot_runs is larger zero, an outer bootstrap will be executed. In the bootstrap validation routine boot_runs bootstrap samples are generated and each one is again bootstrapped boot_cut times. This may lead to long run times, so activating the built-in parallelization may be advisable.

The advantages of bootstrapping the optimal cutpoint are that the procedure doesn’t possess parameters that have to be tuned, unlike the LOESS smoothing, that it doesn’t rely on assumptions, unlike the Normal method, and that it is applicable to any metric that can be used with minimize_metric or maximize_metric, unlike the Kernel method. Furthermore, like Random Forests cannot be overfit by increasing the number of trees, the bootstrapped cutpoints cannot be overfit by running an excessive amount of boot_cut repetitions.

library(cutpointr)
set.seed(100)
cutpointr(suicide, dsi, suicide, gender, 
          method = maximize_boot_metric,
          boot_cut = 200, summary_func = mean,
          metric = accuracy, silent = TRUE)
## # A tibble: 2 × 18
##   subgroup direction optimal_cutpoint method               accuracy      acc
##   <chr>    <chr>                <dbl> <chr>                   <dbl>    <dbl>
## 1 female   >=                 5.73246 maximize_boot_metric 0.956633 0.956633
## 2 male     >=                 8.41026 maximize_boot_metric 0.95     0.95    
##   sensitivity specificity      AUC pos_class neg_class prevalence outcome
##         <dbl>       <dbl>    <dbl> <fct>     <fct>          <dbl> <chr>  
## 1    0.444444    0.994521 0.944647 yes       no         0.0688776 suicide
## 2    0.222222    1        0.861747 yes       no         0.0642857 suicide
##   predictor grouping data               roc_curve           boot 
##   <chr>     <chr>    <list>             <list>              <lgl>
## 1 dsi       gender   <tibble [392 × 2]> <rc_ctpnt [11 × 9]> NA   
## 2 dsi       gender   <tibble [140 × 2]> <rc_ctpnt [11 × 9]> NA

LOESS smoothing for selecting a cutpoint

When using maximize_metric and minimize_metric the optimal cutpoint is selected by searching the maximum or minimum of the metric function. For example, we may want to minimize the misclassification cost. Since false negatives (a suicide attempt was not anticipated) can be regarded as much more severe than false positives we can set the cost of a false negative cost_fn for example to ten times the cost of a false positive.

opt_cut <- cutpointr(suicide, dsi, suicide, gender, method = minimize_metric,
                     metric = misclassification_cost, cost_fp = 1, cost_fn = 10)
## Assuming the positive class is yes
## Assuming the positive class has higher x values
plot_metric(opt_cut)

As this “optimal” cutpoint may depend on minor differences between the possible cutoffs, smoothing of the function of metric values by cutpoint value might be desirable, especially in small samples. The minimize_loess_metric and maximize_loess_metric functions can be used to smooth the function so that the optimal cutpoint is selected based on the smoothed metric values. Options to modify the smoothing, which is implemented using loess.as from the fANCOVA package, include:

  • criterion: the criterion for automatic smoothing parameter selection: “aicc” denotes bias-corrected AIC criterion, “gcv” denotes generalized cross-validation.
  • degree: the degree of the local polynomials to be used. It can be 0, 1 or 2.
  • family: if “gaussian” fitting is by least-squares, and if “symmetric” a re-descending M estimator is used with Tukey’s biweight function.
  • user.span: the user-defined parameter which controls the degree of smoothing.

Using parameters for the LOESS smoothing of criterion = "aicc", degree = 2, family = "symmetric", and user.span = 0.7 we get the following smoothed versions of the above metrics:

opt_cut <- cutpointr(suicide, dsi, suicide, gender, 
                     method = minimize_loess_metric,
                     criterion = "aicc", family = "symmetric", 
                     degree = 2, user.span = 0.7,
                     metric = misclassification_cost, cost_fp = 1, cost_fn = 10)
plot_metric(opt_cut)

The optimal cutpoint for the female subgroup changes to 3. Note, though, that there are no reliable rules for selecting the “best” smoothing parameters. Notably, the LOESS smoothing is sensitive to the number of unique cutpoints. A large number of unique cutpoints generally leads to a more volatile curve of metric values by cutpoint value, even after smoothing. Thus, the curve tends to be undersmoothed in that scenario. The unsmoothed metric values are returned in opt_cut$roc_curve in the column m_unsmoothed.

Smoothing via Generalized Additive Models for selecting a cutpoint

In a similar fashion, the function of metric values per cutpoint can be smoothed using Generalized Additive Models with smooth terms. Internally, mgcv::gam carries out the smoothing which can be customized via the arguments formula and optimizer, see help("gam", package = "mgcv"). Most importantly, the GAM can be specified by altering the default formula, for example the smoothing function could be configured to apply cubic regression splines ("cr") as the smooth term. As the suicide data has only very few unique cutpoints, it is not very suitable for showcasing the GAM smoothing, so we will use two classes of the iris data here. In this case, the purely empirical method and the GAM smoothing lead to identical cutpoints, but in practice the GAM smoothing tends to be more robust, especially with larger data. An attractive feature of the GAM smoothing is that the default values tend to work quite well and usually require no tuning, eliminating researcher degrees of freedom.

library(ggplot2)
exdat <- iris
exdat <- exdat[exdat$Species != "setosa", ]
opt_cut <- cutpointr(exdat, Petal.Length, Species,
                     method = minimize_gam_metric,
                     formula = m ~ s(x.sorted, bs = "cr"),
                     metric = abs_d_sens_spec)
## Assuming the positive class is virginica
## Assuming the positive class has higher x values
plot_metric(opt_cut)

Parametric method assuming normality

The Normal method in oc_youden_normal is a parametric method for maximizing the Youden-Index or equivalently the sum of \(Se\) and \(Sp\). It relies on the assumption that the predictor for both the negative and positive observations is normally distributed. In that case it can be shown that

\[c^* = \frac{(\mu_P \sigma_N^2 - \mu_N \sigma_P^2) - \sigma_N \sigma_P \sqrt{(\mu_N - \mu_P)^2 + (\sigma_N^2 - \sigma_P^2) log(\sigma_N^2 / \sigma_P^2)}}{\sigma_N^2 - \sigma_P^2}\]

where the negative class is normally distributed with \(\sim N(\mu_N, \sigma_N^2)\) and the positive class independently normally distributed with \(\sim N(\mu_P, \sigma_P^2)\) provides the optimal cutpoint \(c^*\) that maximizes the Youden-Index. If \(\sigma_N\) and \(\sigma_P\) are equal, the expression can be simplified to \(c^* = \frac{\mu_N + \mu_P}{2}\). However, the oc_youden_normal method in cutpointr always assumes unequal standard deviations. Since this method does not select a cutpoint from the observed predictor values, it is questionable which values for \(Se\) and \(Sp\) should be reported. Here, the Youden-Index can be calculated as

\[J = \Phi(\frac{c^* - \mu_N}{\sigma_N}) - \Phi(\frac{c^* - \mu_P}{\sigma_P})\]

if the assumption of normality holds. However, since there exist several methods that do not select cutpoints from the available observations and to unify the reporting of metrics for these methods, cutpointr reports all metrics, e.g. \(Se\) and \(Sp\), based on the empirical observations.

cutpointr(suicide, dsi, suicide, gender, method = oc_youden_normal)
## Assuming the positive class is yes
## Assuming the positive class has higher x values
## # A tibble: 2 × 18
##   subgroup direction optimal_cutpoint method           sum_sens_spec      acc
##   <chr>    <chr>                <dbl> <chr>                    <dbl>    <dbl>
## 1 female   >=                 2.47775 oc_youden_normal       1.71618 0.895408
## 2 male     >=                 3.17226 oc_youden_normal       1.54453 0.864286
##   sensitivity specificity      AUC pos_class neg_class prevalence outcome
##         <dbl>       <dbl>    <dbl> <fct>     <fct>          <dbl> <chr>  
## 1    0.814815    0.901370 0.944647 yes       no         0.0688776 suicide
## 2    0.666667    0.877863 0.861747 yes       no         0.0642857 suicide
##   predictor grouping data               roc_curve           boot 
##   <chr>     <chr>    <list>             <list>              <lgl>
## 1 dsi       gender   <tibble [392 × 2]> <rc_ctpnt [11 × 9]> NA   
## 2 dsi       gender   <tibble [140 × 2]> <rc_ctpnt [11 × 9]> NA

Nonparametric kernel method

A nonparametric alternative is the Kernel method [@fluss_estimation_2005]. Here, the empirical distribution functions are smoothed using the Gaussian kernel functions \(\hat{F}_N(t) = \frac{1}{n} \sum^n_{i=1} \Phi(\frac{t - y_i}{h_y})\) and \(\hat{G}_P(t) = \frac{1}{m} \sum^m_{i=1} \Phi(\frac{t - x_i}{h_x})\) for the negative and positive classes respectively. Following Silverman’s plug-in “rule of thumb” the bandwidths are selected as \(h_y = 0.9 * min\{s_y, iqr_y/1.34\} * n^{-0.2}\) and \(h_x = 0.9 * min\{s_x, iqr_x/1.34\} * m^{-0.2}\) where \(s\) is the sample standard deviation and \(iqr\) is the inter quartile range. It has been demonstrated that AUC estimation is rather insensitive to the choice of the bandwidth procedure [@faraggi_estimation_2002] and thus the plug-in bandwidth estimator has also been recommended for cutpoint estimation. The oc_youden_kernel function in cutpointr uses a Gaussian kernel and the direct plug-in method for selecting the bandwidths. The kernel smoothing is done via the bkde function from the KernSmooth package [@wand_kernsmooth:_2013].

Again, there is a way to calculate the Youden-Index from the results of this method [@fluss_estimation_2005] which is

\[\hat{J} = max_c \{\hat{F}_N(c) - \hat{G}_N(c) \}\]

but as before we prefer to report all metrics based on applying the cutpoint that was estimated using the Kernel method to the empirical observations.

cutpointr(suicide, dsi, suicide, gender, method = oc_youden_kernel)
## Assuming the positive class is yes
## Assuming the positive class has higher x values
## # A tibble: 2 × 18
##   subgroup direction optimal_cutpoint method           sum_sens_spec      acc
##   <chr>    <chr>                <dbl> <chr>                    <dbl>    <dbl>
## 1 female   >=                 1.18128 oc_youden_kernel       1.80812 0.885204
## 2 male     >=                 1.31636 oc_youden_kernel       1.58694 0.807143
##   sensitivity specificity      AUC pos_class neg_class prevalence outcome
##         <dbl>       <dbl>    <dbl> <fct>     <fct>          <dbl> <chr>  
## 1    0.925926    0.882192 0.944647 yes       no         0.0688776 suicide
## 2    0.777778    0.809160 0.861747 yes       no         0.0642857 suicide
##   predictor grouping data               roc_curve           boot 
##   <chr>     <chr>    <list>             <list>              <lgl>
## 1 dsi       gender   <tibble [392 × 2]> <rc_ctpnt [11 × 9]> NA   
## 2 dsi       gender   <tibble [140 × 2]> <rc_ctpnt [11 × 9]> NA
cutpointr/inst/doc/cutpointr_user_functions.Rmd0000644000176200001440000001006714726066534021665 0ustar liggesusers--- title: "User-defined functions for estimation methods and metrics" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{User-defined functions for estimation methods and metrics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` # User-defined functions ## method User-defined functions can be supplied to `method`, which is the function that is responsible for returning the optimal cutpoint. To define a new method function, create a function that may take as input(s): - `data`: A `data.frame` or `tbl_df` - `x`: (character) The name of the predictor variable - `class`: (character) The name of the class variable - `metric_func`: A function for calculating a metric, e.g. accuracy. Note that the method function does not necessarily have to accept this argument - `pos_class`: The positive class - `neg_class`: The negative class - `direction`: `">="` if the positive class has higher x values, `"<="` otherwise - `tol_metric`: (numeric) In the built-in methods, all cutpoints will be returned that lead to a metric value in the interval [m_max - tol_metric, m_max + tol_metric] where m_max is the maximum achievable metric value. This can be used to return multiple decent cutpoints and to avoid floating-point problems. - `use_midpoints`: (logical) In the built-in methods, if TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = ">") or the next lowest observation (for direction = "<") which avoids biasing the optimal cutpoint. - `...`: Further arguments that are passed to `metric` or that can be captured inside of `method` The function should return a data frame or tibble with one row, the column `optimal_cutpoint`, and an optional column with an arbitrary name with the metric value at the optimal cutpoint. For example, a function for choosing the cutpoint as the mean of the independent variable could look like this: ```{r, eval = FALSE} mean_cut <- function(data, x, ...) { oc <- mean(data[[x]]) return(data.frame(optimal_cutpoint = oc)) } ``` If a `method` function does not return a metric column, the default `sum_sens_spec`, the sum of sensitivity and specificity, is returned as the extra metric column in addition to accuracy, sensitivity and specificity. Some `method` functions that make use of the additional arguments (that are captured by `...`) are already included in **cutpointr**, see the list at the top. Since these functions are arguments to `cutpointr` their code can be accessed by simply typing their name, see for example `oc_youden_normal`. ## metric User defined `metric` functions can be used as well. They are mainly useful in conjunction with `method = maximize_metric`, `method = minimize_metric`, or one of the other minimization and maximization functions. In case of a different `method` function `metric` will only be used as the main out-of-bag metric when plotting the result. The `metric` function should accept the following inputs as vectors: - `tp`: Vector of true positives - `fp`: Vector of false positives - `tn`: Vector of true negatives - `fn`: Vector of false negatives - `...`: Further arguments The function should return a numeric vector, a matrix, or a `data.frame` with one column. If the column is named, the name will be included in the output and plots. Avoid using names that are identical to the column names that are by default returned by **cutpointr**, as such names will be prefixed by `metric_` in the output. The inputs (`tp`, `fp`, `tn`, and `fn`) are vectors. The code of the included metric functions can be accessed by simply typing their name. For example, this is the `misclassification_cost` metric function: ```{r} library(cutpointr) misclassification_cost ``` cutpointr/inst/doc/cutpointr_plotting.R0000644000176200001440000000307514726136575020142 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ## ----fig.width=4, fig.height=3------------------------------------------------ library(cutpointr) set.seed(123) opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500) plot_cut_boot(opt_cut_b_g) plot_metric(opt_cut_b_g, conf_lvl = 0.9) plot_metric_boot(opt_cut_b_g) plot_precision_recall(opt_cut_b_g) plot_sensitivity_specificity(opt_cut_b_g) plot_roc(opt_cut_b_g) ## ----fig.width=4, fig.height=3------------------------------------------------ library(ggplot2) p <- plot_x(opt_cut_b_g) p + ggtitle("Distribution of dsi") + theme_minimal() + xlab("Depression score") ## ----fig.width=4, fig.height=3, cache=FALSE----------------------------------- set.seed(1234) opt_cut_b <- cutpointr(suicide, dsi, suicide, boot_runs = 500) plot_cutpointr(opt_cut_b, xvar = cutpoints, yvar = sum_sens_spec, conf_lvl = 0.9) plot_cutpointr(opt_cut_b, xvar = fpr, yvar = tpr, aspect_ratio = 1, conf_lvl = 0) plot_cutpointr(opt_cut_b, xvar = cutpoint, yvar = tp, conf_lvl = 0.9) + geom_point() ## ----fig.width=4, fig.height=3------------------------------------------------ library(dplyr) library(tidyr) opt_cut_b_g |> select(data, subgroup) |> unnest(cols = data) |> ggplot(aes(x = suicide, y = dsi)) + geom_boxplot(alpha = 0.3) + facet_grid(~subgroup) cutpointr/inst/doc/cutpointr_roc.html0000644000176200001440000006173214726136576017635 0ustar liggesusers ROC curves with cutpointr

ROC curves with cutpointr

Christian Thiele

2024-12-10

Calculating only the ROC curve

When running cutpointr, a ROC curve is by default returned in the column roc_curve. This ROC curve can be plotted using plot_roc. Alternatively, if only the ROC curve is desired and no cutpoint needs to be calculated, the ROC curve can be created using roc() and plotted using plot_cutpointr. The roc function, unlike cutpointr, does not determine direction, pos_class or neg_class automatically.

library(cutpointr)
roc_curve <- roc(data = suicide, x = dsi, class = suicide,
    pos_class = "yes", neg_class = "no", direction = ">=")
auc(roc_curve)
## [1] 0.9237791
head(roc_curve)
## # A tibble: 6 × 9
##   x.sorted    tp    fp    tn    fn    tpr   tnr     fpr   fnr
##      <dbl> <dbl> <dbl> <int> <int>  <dbl> <dbl>   <dbl> <dbl>
## 1      Inf     0     0   496    36 0      1     0       1    
## 2       11     1     0   496    35 0.0278 1     0       0.972
## 3       10     2     1   495    34 0.0556 0.998 0.00202 0.944
## 4        9     3     1   495    33 0.0833 0.998 0.00202 0.917
## 5        8     4     1   495    32 0.111  0.998 0.00202 0.889
## 6        7     7     1   495    29 0.194  0.998 0.00202 0.806
plot_roc(roc_curve)

ROC curve and optimal cutpoint for multiple variables

Alternatively, we can map the standard evaluation version cutpointr to the column names. If direction and / or pos_class and neg_class are unspecified, these parameters will automatically be determined by cutpointr so that the AUC values for all variables will be \(> 0.5\).

We could do this manually, e.g. using purrr::map, but to make this task more convenient multi_cutpointr can be used to achieve the same result. It maps multiple predictor columns to cutpointr, by default all numeric columns except for the class column.

mcp <- multi_cutpointr(suicide, class = suicide, pos_class = "yes", 
                use_midpoints = TRUE, silent = TRUE) 
summary(mcp)
## Method: maximize_metric 
## Predictor: age, dsi 
## Outcome: suicide 
## 
## Predictor: age 
## -------------------------------------------------------------------------------- 
##  direction    AUC   n n_pos n_neg
##         <= 0.5257 532    36   496
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn  fp tn
##              55.5        1.1154 0.1992      0.9722      0.1431 35  1 425 71
## 
## Predictor summary: 
##     Data Min. 5% 1st Qu. Median    Mean 3rd Qu.   95% Max.      SD NAs
##  Overall   18 19      24   28.0 34.1259   41.25 65.00   83 15.0542   0
##       no   18 19      24   28.0 34.2218   41.25 65.50   83 15.1857   0
##      yes   18 18      22   27.5 32.8056   41.25 54.25   69 13.2273   0
## 
## Predictor: dsi 
## -------------------------------------------------------------------------------- 
##  direction    AUC   n n_pos n_neg
##         >= 0.9238 532    36   496
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##               1.5        1.7518 0.8647      0.8889      0.8629 32  4 68 428
## 
## Predictor summary: 
##     Data Min.   5% 1st Qu. Median   Mean 3rd Qu.  95% Max.     SD NAs
##  Overall    0 0.00       0      0 0.9211       1 5.00   11 1.8527   0
##       no    0 0.00       0      0 0.6331       0 4.00   10 1.4122   0
##      yes    0 0.75       4      5 4.8889       6 9.25   11 2.5498   0

Accessing data, roc_curve, and boot

The object returned by cutpointr is of the classes cutpointr, tbl_df, tbl, and data.frame. Thus, it can be handled like a usual data frame. The columns data, roc_curve, and boot consist of nested data frames, which means that these are list columns whose elements are data frames. They can either be accessed using [ or by using functions from the tidyverse. If subgroups were given, the output contains one row per subgroup and the function that accesses the data should be mapped to every row or the data should be grouped by subgroup.

set.seed(123)
opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500)
library(dplyr)
library(tidyr)
opt_cut_b_g |> 
  group_by(subgroup) |> 
  select(subgroup, boot) |> 
  unnest(cols = boot) |> 
  summarise(sd_oc_boot = sd(optimal_cutpoint),
            m_oc_boot  = mean(optimal_cutpoint),
            m_acc_oob  = mean(acc_oob))
## # A tibble: 2 × 4
##   subgroup sd_oc_boot m_oc_boot m_acc_oob
##   <chr>         <dbl>     <dbl>     <dbl>
## 1 female        0.766      2.17     0.880
## 2 male          1.51       2.92     0.806

Adding metrics to the result of cutpointr() or roc()

By default, the output of cutpointr includes the optimized metric and several other metrics. The add_metric function adds further metrics. Here, we’re adding the negative predictive value (NPV) and the positive predictive value (PPV) at the optimal cutpoint per subgroup:

cutpointr(suicide, dsi, suicide, gender, metric = youden, silent = TRUE) |> 
    add_metric(list(ppv, npv)) |> 
    select(subgroup, optimal_cutpoint, youden, ppv, npv)
## # A tibble: 2 × 5
##   subgroup optimal_cutpoint   youden      ppv      npv
##   <chr>               <dbl>    <dbl>    <dbl>    <dbl>
## 1 female                  2 0.808118 0.367647 0.993827
## 2 male                    3 0.625106 0.259259 0.982301

In the same fashion, additional metric columns can be added to a roc_cutpointr object:

roc(data = suicide, x = dsi, class = suicide, pos_class = "yes",
    neg_class = "no", direction = ">=") |> 
  add_metric(list(cohens_kappa, F1_score)) |> 
  select(x.sorted, tp, fp, tn, fn, cohens_kappa, F1_score) |> 
  head()
## # A tibble: 6 × 7
##   x.sorted    tp    fp    tn    fn cohens_kappa F1_score
##      <dbl> <dbl> <dbl> <int> <int>        <dbl>    <dbl>
## 1      Inf     0     0   496    36       0        0     
## 2       11     1     0   496    35       0.0506   0.0541
## 3       10     2     1   495    34       0.0931   0.103 
## 4        9     3     1   495    33       0.138    0.15  
## 5        8     4     1   495    32       0.182    0.195 
## 6        7     7     1   495    29       0.301    0.318
cutpointr/inst/doc/cutpointr_plotting.html0000644000176200001440000025560014726136575020710 0ustar liggesusers Plotting with cutpointr

Plotting with cutpointr

Christian Thiele

2024-12-10

cutpointr includes several convenience functions for plotting data from a cutpointr object. These include:

  • plot_cutpointr: General purpose plotting function for cutpointr or roc_cutpointr objects
  • plot_cut_boot: Plot the bootstrapped distribution of optimal cutpoints
  • plot_metric: If maximize_metric or minimize_metric was used this function plots all possible cutoffs on the x-axis vs. the respective metric values on the y-axis. If bootstrapping was run, a confidence interval based on the bootstrapped distribution of metric values at each cutpoint can be displayed. To display no confidence interval set conf_lvl = 0.
  • plot_metric_boot: Plot the distribution of out-of-bag metric values
  • plot_precision_recall: Plot the precision recall curve
  • plot_sensitivity_specificity: Plot all cutpoints vs. sensitivity and specificity
  • plot_roc: Plot the ROC curve
  • plot_x: Plot the distribution of the predictor variable
library(cutpointr)

set.seed(123)
opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500)
## Assuming the positive class is yes
## Assuming the positive class has higher x values
## Running bootstrap...
plot_cut_boot(opt_cut_b_g)
## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).

plot_metric(opt_cut_b_g, conf_lvl = 0.9)

plot_metric_boot(opt_cut_b_g)
## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_density()`).

plot_precision_recall(opt_cut_b_g)

plot_sensitivity_specificity(opt_cut_b_g)

plot_roc(opt_cut_b_g)

All plot functions, except for the standard plot method that returns a composed plot, return ggplot objects than can be further modified. For example, changing labels, title, and the theme can be achieved this way:

library(ggplot2)
p <- plot_x(opt_cut_b_g)
p + ggtitle("Distribution of dsi") + 
    theme_minimal() + 
    xlab("Depression score")

Flexible plotting function

Using plot_cutpointr any metric can be chosen to be plotted on the x- or y-axis and results of cutpointr() as well as roc() can be plotted. If a cutpointr object is to be plotted, it is thus irrelevant which metric function was chosen for cutpoint estimation. Any metric that can be calculated based on the ROC curve can be subsequently plotted as only the true / false positives / negatives over all cutpoints are needed. That way, not only the above plots can be produced, but also any combination of two metrics (or metric functions) and / or cutpoints. The built-in metric functions as well as user-defined functions or anonymous functions can be supplied to xvar and yvar. If bootstrapping was run, confidence intervals can be plotted around the y-variable. This is especially useful if the cutpoints, available in the cutpoints function, are placed on the x-axis. Note that confidence intervals can only be correctly plotted if the values of xvar are constant across bootstrap samples. For example, confidence intervals for TPR by FPR (a ROC curve) cannot be plotted easily, as the values of the false positive rate vary per bootstrap sample.

set.seed(1234)
opt_cut_b <- cutpointr(suicide, dsi, suicide, boot_runs = 500)
## Assuming the positive class is yes
## Assuming the positive class has higher x values
## Running bootstrap...
plot_cutpointr(opt_cut_b, xvar = cutpoints, yvar = sum_sens_spec, conf_lvl = 0.9)

plot_cutpointr(opt_cut_b, xvar = fpr, yvar = tpr, aspect_ratio = 1, conf_lvl = 0)

plot_cutpointr(opt_cut_b, xvar = cutpoint, yvar = tp, conf_lvl = 0.9) + 
    geom_point()

Manual plotting

Since cutpointr returns a data.frame with the original data, bootstrap results, and the ROC curve in nested tibbles, these data can be conveniently extracted and plotted manually. The relevant nested tibbles are in the columns data, roc_curve and boot. The following is an example of accessing and plotting the grouped data.

library(dplyr)
library(tidyr)
opt_cut_b_g |> 
    select(data, subgroup) |> 
    unnest(cols = data) |> 
    ggplot(aes(x = suicide, y = dsi)) + 
    geom_boxplot(alpha = 0.3) + 
    facet_grid(~subgroup)

cutpointr/inst/doc/cutpointr_benchmarks.R0000644000176200001440000003234314726136472020413 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ## ----eval = FALSE------------------------------------------------------------- # # Return cutpoint that maximizes the sum of sensitivity and specificiy # # ROCR package # rocr_sensspec <- function(x, class) { # pred <- ROCR::prediction(x, class) # perf <- ROCR::performance(pred, "sens", "spec") # sens <- slot(perf, "y.values")[[1]] # spec <- slot(perf, "x.values")[[1]] # cut <- slot(perf, "alpha.values")[[1]] # cut[which.max(sens + spec)] # } # # # pROC package # proc_sensspec <- function(x, class) { # r <- pROC::roc(class, x, algorithm = 2, levels = c(0, 1), direction = "<") # pROC::coords(r, "best", ret="threshold", transpose = FALSE)[1] # } ## ----eval = FALSE, echo = FALSE----------------------------------------------- # library(OptimalCutpoints) # library(ThresholdROC) # library(dplyr) # n <- 100 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # x_pos <- dat$x[dat$y == 1] # x_neg <- dat$x[dat$y == 0] # bench_100 <- microbenchmark::microbenchmark( # cutpointr(dat, x, y, pos_class = 1, neg_class = 0, # direction = ">=", metric = youden, break_ties = mean), # rocr_sensspec(dat$x, dat$y), # proc_sensspec(dat$x, dat$y), # optimal.cutpoints(X = "x", status = "y", tag.healthy = 0, methods = "Youden", # data = dat), # thres2(k1 = x_neg, k2 = x_pos, rho = 0.5, # method = "empirical", ci = FALSE), # times = 100, unit = "ms" # ) # # n <- 1000 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # x_pos <- dat$x[dat$y == 1] # x_neg <- dat$x[dat$y == 0] # bench_1000 <- microbenchmark::microbenchmark( # cutpointr(dat, x, y, pos_class = 1, neg_class = 0, # direction = ">=", metric = youden, break_ties = mean), # rocr_sensspec(dat$x, dat$y), # proc_sensspec(dat$x, dat$y), # optimal.cutpoints(X = "x", status = "y", tag.healthy = 0, methods = "Youden", # data = dat), # thres2(k1 = x_neg, k2 = x_pos, rho = 0.5, # method = "empirical", ci = FALSE), # times = 100, unit = "ms" # ) # # n <- 10000 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # x_pos <- dat$x[dat$y == 1] # x_neg <- dat$x[dat$y == 0] # bench_10000 <- microbenchmark::microbenchmark( # cutpointr(dat, x, y, pos_class = 1, neg_class = 0, # direction = ">=", metric = youden, break_ties = mean, silent = TRUE), # rocr_sensspec(dat$x, dat$y), # optimal.cutpoints(X = "x", status = "y", tag.healthy = 0, methods = "Youden", # data = dat), # proc_sensspec(dat$x, dat$y), # thres2(k1 = x_neg, k2 = x_pos, rho = 0.5, # method = "empirical", ci = FALSE), # times = 100 # ) # # n <- 1e5 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # bench_1e5 <- microbenchmark::microbenchmark( # cutpointr(dat, x, y, pos_class = 1, neg_class = 0, # direction = ">=", metric = youden, break_ties = mean), # rocr_sensspec(dat$x, dat$y), # proc_sensspec(dat$x, dat$y), # times = 100, unit = "ms" # ) # # n <- 1e6 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # bench_1e6 <- microbenchmark::microbenchmark( # cutpointr(dat, x, y, pos_class = 1, neg_class = 0, # direction = ">=", metric = youden, break_ties = mean), # rocr_sensspec(dat$x, dat$y), # proc_sensspec(dat$x, dat$y), # times = 30, unit = "ms" # ) # # n <- 1e7 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # bench_1e7 <- microbenchmark::microbenchmark( # cutpointr(dat, x, y, pos_class = 1, neg_class = 0, # direction = ">=", metric = youden, break_ties = mean), # rocr_sensspec(dat$x, dat$y), # proc_sensspec(dat$x, dat$y), # times = 30, unit = "ms" # ) # # results <- rbind( # data.frame(time = summary(bench_100)$median, # Solution = summary(bench_100)$expr, # n = 100), # data.frame(time = summary(bench_1000)$median, # Solution = summary(bench_1000)$expr, # n = 1000), # data.frame(time = summary(bench_10000)$median, # Solution = summary(bench_10000)$expr, # n = 10000), # data.frame(time = summary(bench_1e5)$median, # Solution = summary(bench_1e5)$expr, # n = 1e5), # data.frame(time = summary(bench_1e6)$median, # Solution = summary(bench_1e6)$expr, # n = 1e6), # data.frame(time = summary(bench_1e7)$median, # Solution = summary(bench_1e7)$expr, # n = 1e7) # ) # results$Solution <- as.character(results$Solution) # results$Solution[grep(pattern = "cutpointr", x = results$Solution)] <- "cutpointr" # results$Solution[grep(pattern = "rocr", x = results$Solution)] <- "ROCR" # results$Solution[grep(pattern = "optimal", x = results$Solution)] <- "OptimalCutpoints" # results$Solution[grep(pattern = "proc", x = results$Solution)] <- "pROC" # results$Solution[grep(pattern = "thres", x = results$Solution)] <- "ThresholdROC" # # results$task <- "Cutpoint Estimation" ## ----echo = FALSE------------------------------------------------------------- # These are the original results on our system # dput(results) results <- structure(list(time = c(4.5018015, 1.812802, 0.662101, 2.2887015, 1.194301, 4.839401, 2.1764015, 0.981001, 45.0568005, 36.2398515, 8.5662515, 5.667101, 2538.612001, 4.031701, 2503.8012505, 45.384501, 43.118751, 37.150151, 465.003201, 607.023851, 583.0950005, 5467.332801, 7850.2587, 7339.356101), Solution = c("cutpointr", "ROCR", "pROC", "OptimalCutpoints", "ThresholdROC", "cutpointr", "ROCR", "pROC", "OptimalCutpoints", "ThresholdROC", "cutpointr", "ROCR", "OptimalCutpoints", "pROC", "ThresholdROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC"), n = c(100, 100, 100, 100, 100, 1000, 1000, 1000, 1000, 1000, 10000, 10000, 10000, 10000, 10000, 1e+05, 1e+05, 1e+05, 1e+06, 1e+06, 1e+06, 1e+07, 1e+07, 1e+07), task = c("Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation")), row.names = c(NA, -24L), class = "data.frame") ## ----eval = FALSE------------------------------------------------------------- # # ROCR package # rocr_roc <- function(x, class) { # pred <- ROCR::prediction(x, class) # perf <- ROCR::performance(pred, "sens", "spec") # return(NULL) # } # # # pROC package # proc_roc <- function(x, class) { # r <- pROC::roc(class, x, algorithm = 2, levels = c(0, 1), direction = "<") # return(NULL) # } ## ----eval = FALSE, echo = FALSE----------------------------------------------- # n <- 100 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # bench_100 <- microbenchmark::microbenchmark( # cutpointr::roc(dat, "x", "y", pos_class = 1, # neg_class = 0, direction = ">="), # rocr_roc(dat$x, dat$y), # proc_roc(dat$x, dat$y), # times = 100, unit = "ms" # ) # n <- 1000 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # bench_1000 <- microbenchmark::microbenchmark( # cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, # direction = ">="), # rocr_roc(dat$x, dat$y), # proc_roc(dat$x, dat$y), # times = 100, unit = "ms" # ) # n <- 10000 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # bench_10000 <- microbenchmark::microbenchmark( # cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, # direction = ">="), # rocr_roc(dat$x, dat$y), # proc_roc(dat$x, dat$y), # times = 100, unit = "ms" # ) # n <- 1e5 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # bench_1e5 <- microbenchmark::microbenchmark( # cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, # direction = ">="), # rocr_roc(dat$x, dat$y), # proc_roc(dat$x, dat$y), # times = 100, unit = "ms" # ) # n <- 1e6 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # bench_1e6 <- microbenchmark::microbenchmark( # cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, # direction = ">="), # rocr_roc(dat$x, dat$y), # proc_roc(dat$x, dat$y), # times = 30, unit = "ms" # ) # n <- 1e7 # set.seed(123) # dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) # bench_1e7 <- microbenchmark::microbenchmark( # cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, # direction = ">="), # rocr_roc(dat$x, dat$y), # proc_roc(dat$x, dat$y), # times = 30, unit = "ms" # ) # # results_roc <- rbind( # data.frame(time = summary(bench_100)$median, # Solution = summary(bench_100)$expr, # n = 100), # data.frame(time = summary(bench_1000)$median, # Solution = summary(bench_1000)$expr, # n = 1000), # data.frame(time = summary(bench_10000)$median, # Solution = summary(bench_10000)$expr, # n = 10000), # data.frame(time = summary(bench_1e5)$median, # Solution = summary(bench_1e5)$expr, # n = 1e5), # data.frame(time = summary(bench_1e6)$median, # Solution = summary(bench_1e6)$expr, # n = 1e6), # data.frame(time = summary(bench_1e7)$median, # Solution = summary(bench_1e7)$expr, # n = 1e7) # ) # results_roc$Solution <- as.character(results_roc$Solution) # results_roc$Solution[grep(pattern = "cutpointr", x = results_roc$Solution)] <- "cutpointr" # results_roc$Solution[grep(pattern = "rocr", x = results_roc$Solution)] <- "ROCR" # results_roc$Solution[grep(pattern = "proc", x = results_roc$Solution)] <- "pROC" # results_roc$task <- "ROC curve calculation" ## ----echo = FALSE------------------------------------------------------------- # Our results results_roc <- structure(list(time = c(0.7973505, 1.732651, 0.447701, 0.859301, 2.0358515, 0.694802, 1.878151, 5.662151, 3.6580505, 11.099251, 42.8208515, 35.3293005, 159.8100505, 612.471901, 610.4337005, 2032.693551, 7806.3854515, 7081.897251), Solution = c("cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC"), n = c(100, 100, 100, 1000, 1000, 1000, 10000, 10000, 10000, 1e+05, 1e+05, 1e+05, 1e+06, 1e+06, 1e+06, 1e+07, 1e+07, 1e+07), task = c("ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation")), row.names = c(NA, -18L), class = "data.frame") ## ----echo = FALSE------------------------------------------------------------- library(ggplot2) results_all <- dplyr::bind_rows(results, results_roc) ggplot(results_all, aes(x = n, y = time, col = Solution, shape = Solution)) + geom_point(size = 3) + geom_line() + scale_y_log10(breaks = c(0.5, 1, 2, 3, 5, 10, 25, 100, 250, 1000, 5000, 1e4, 15000)) + scale_x_log10(breaks = c(100, 1000, 1e4, 1e5, 1e6, 1e7)) + ylab("Median Time (milliseconds, log scale)") + xlab("Sample Size (log scale)") + theme_bw() + theme(legend.position = "bottom", legend.key.width = unit(0.8, "cm"), panel.spacing = unit(1, "lines")) + facet_grid(~task) ## ----echo = FALSE------------------------------------------------------------- library(tidyr) res_table <- tidyr::spread(results_all, Solution, time) res_table <- dplyr::arrange(res_table, task) knitr::kable(res_table) cutpointr/inst/doc/cutpointr_roc.Rmd0000644000176200001440000000744414726066534017407 0ustar liggesusers--- title: "ROC curves with cutpointr" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{ROC curves with cutpointr} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` ## Calculating only the ROC curve When running `cutpointr`, a ROC curve is by default returned in the column `roc_curve`. This ROC curve can be plotted using `plot_roc`. Alternatively, if only the ROC curve is desired and no cutpoint needs to be calculated, the ROC curve can be created using `roc()` and plotted using `plot_cutpointr`. The `roc` function, unlike `cutpointr`, does not determine `direction`, `pos_class` or `neg_class` automatically. ```{r, fig.width=4, fig.height=3} library(cutpointr) roc_curve <- roc(data = suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") auc(roc_curve) head(roc_curve) plot_roc(roc_curve) ``` ## ROC curve and optimal cutpoint for multiple variables Alternatively, we can map the standard evaluation version `cutpointr` to the column names. If `direction` and / or `pos_class` and `neg_class` are unspecified, these parameters will automatically be determined by **cutpointr** so that the AUC values for all variables will be $> 0.5$. We could do this manually, e.g. using `purrr::map`, but to make this task more convenient `multi_cutpointr` can be used to achieve the same result. It maps multiple predictor columns to `cutpointr`, by default all numeric columns except for the class column. ```{r} mcp <- multi_cutpointr(suicide, class = suicide, pos_class = "yes", use_midpoints = TRUE, silent = TRUE) summary(mcp) ``` ## Accessing `data`, `roc_curve`, and `boot` The object returned by `cutpointr` is of the classes `cutpointr`, `tbl_df`, `tbl`, and `data.frame`. Thus, it can be handled like a usual data frame. The columns `data`, `roc_curve`, and `boot` consist of nested data frames, which means that these are list columns whose elements are data frames. They can either be accessed using `[` or by using functions from the tidyverse. If subgroups were given, the output contains one row per subgroup and the function that accesses the data should be mapped to every row or the data should be grouped by subgroup. ```{r, eval = FALSE, message = FALSE} set.seed(123) opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500) ``` ```{r, message = FALSE} library(dplyr) library(tidyr) opt_cut_b_g |> group_by(subgroup) |> select(subgroup, boot) |> unnest(cols = boot) |> summarise(sd_oc_boot = sd(optimal_cutpoint), m_oc_boot = mean(optimal_cutpoint), m_acc_oob = mean(acc_oob)) ``` ## Adding metrics to the result of cutpointr() or roc() By default, the output of `cutpointr` includes the optimized metric and several other metrics. The `add_metric` function adds further metrics. Here, we're adding the negative predictive value (NPV) and the positive predictive value (PPV) at the optimal cutpoint per subgroup: ```{r} cutpointr(suicide, dsi, suicide, gender, metric = youden, silent = TRUE) |> add_metric(list(ppv, npv)) |> select(subgroup, optimal_cutpoint, youden, ppv, npv) ``` In the same fashion, additional metric columns can be added to a `roc_cutpointr` object: ```{r} roc(data = suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") |> add_metric(list(cohens_kappa, F1_score)) |> select(x.sorted, tp, fp, tn, fn, cohens_kappa, F1_score) |> head() ``` cutpointr/inst/doc/cutpointr_benchmarks.Rmd0000644000176200001440000003462714726066534020744 0ustar liggesusers--- title: "cutpointr benchmarks" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{cutpointr benchmarks} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` To offer a comparison to established solutions, **cutpointr** will be benchmarked against `optimal.cutpoints` from the **OptimalCutpoints** package, **ThresholdROC** and custom functions based on the **ROCR** and **pROC** packages. By generating data of different sizes the benchmarks will offer a comparison of the scalability of the different solutions. Using `prediction` and `performance` from the **ROCR** package and `roc` from the **pROC** package, we can write functions for computing the cutpoint that maximizes the sum of sensitivity and specificity. **pROC** has a built-in function to optimize a few metrics: ```{r, eval = FALSE} # Return cutpoint that maximizes the sum of sensitivity and specificiy # ROCR package rocr_sensspec <- function(x, class) { pred <- ROCR::prediction(x, class) perf <- ROCR::performance(pred, "sens", "spec") sens <- slot(perf, "y.values")[[1]] spec <- slot(perf, "x.values")[[1]] cut <- slot(perf, "alpha.values")[[1]] cut[which.max(sens + spec)] } # pROC package proc_sensspec <- function(x, class) { r <- pROC::roc(class, x, algorithm = 2, levels = c(0, 1), direction = "<") pROC::coords(r, "best", ret="threshold", transpose = FALSE)[1] } ``` The benchmarking will be carried out using the **microbenchmark** package and randomly generated data. The values of the `x` predictor variable are drawn from a normal distribution which leads to a lot more unique values than were encountered before in the `suicide` data. Accordingly, the search for an optimal cutpoint is much more demanding, if all possible cutpoints are evaluated. Benchmarks are run for sample sizes of 100, 1000, 1e4, 1e5, 1e6, and 1e7. For low sample sizes **cutpointr** is slower than the other solutions. While this should be of low practical importance, **cutpointr** scales more favorably with increasing sample size. The speed disadvantage in small samples that leads to the lower limit of around 25ms is mainly due to the nesting of the original data and the results that makes the compact output of `cutpointr` possible. This observation is emphasized by the fact that `cutpointr::roc` is quite fast also in small samples. For sample sizes > 1e5 **cutpointr** is a little faster than the function based on **ROCR** and **pROC**. Both of these solutions are generally faster than **OptimalCutpoints** and **ThresholdROC** with the exception of small samples. **OptimalCutpoints** and **ThresholdROC** had to be excluded from benchmarks with more than 1e4 observations due to high memory requirements and/or excessive run times, rendering the use of these packages in larger samples impractical. ```{r, eval = FALSE, echo = FALSE} library(OptimalCutpoints) library(ThresholdROC) library(dplyr) n <- 100 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) x_pos <- dat$x[dat$y == 1] x_neg <- dat$x[dat$y == 0] bench_100 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), optimal.cutpoints(X = "x", status = "y", tag.healthy = 0, methods = "Youden", data = dat), thres2(k1 = x_neg, k2 = x_pos, rho = 0.5, method = "empirical", ci = FALSE), times = 100, unit = "ms" ) n <- 1000 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) x_pos <- dat$x[dat$y == 1] x_neg <- dat$x[dat$y == 0] bench_1000 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), optimal.cutpoints(X = "x", status = "y", tag.healthy = 0, methods = "Youden", data = dat), thres2(k1 = x_neg, k2 = x_pos, rho = 0.5, method = "empirical", ci = FALSE), times = 100, unit = "ms" ) n <- 10000 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) x_pos <- dat$x[dat$y == 1] x_neg <- dat$x[dat$y == 0] bench_10000 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean, silent = TRUE), rocr_sensspec(dat$x, dat$y), optimal.cutpoints(X = "x", status = "y", tag.healthy = 0, methods = "Youden", data = dat), proc_sensspec(dat$x, dat$y), thres2(k1 = x_neg, k2 = x_pos, rho = 0.5, method = "empirical", ci = FALSE), times = 100 ) n <- 1e5 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e5 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), times = 100, unit = "ms" ) n <- 1e6 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e6 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), times = 30, unit = "ms" ) n <- 1e7 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e7 <- microbenchmark::microbenchmark( cutpointr(dat, x, y, pos_class = 1, neg_class = 0, direction = ">=", metric = youden, break_ties = mean), rocr_sensspec(dat$x, dat$y), proc_sensspec(dat$x, dat$y), times = 30, unit = "ms" ) results <- rbind( data.frame(time = summary(bench_100)$median, Solution = summary(bench_100)$expr, n = 100), data.frame(time = summary(bench_1000)$median, Solution = summary(bench_1000)$expr, n = 1000), data.frame(time = summary(bench_10000)$median, Solution = summary(bench_10000)$expr, n = 10000), data.frame(time = summary(bench_1e5)$median, Solution = summary(bench_1e5)$expr, n = 1e5), data.frame(time = summary(bench_1e6)$median, Solution = summary(bench_1e6)$expr, n = 1e6), data.frame(time = summary(bench_1e7)$median, Solution = summary(bench_1e7)$expr, n = 1e7) ) results$Solution <- as.character(results$Solution) results$Solution[grep(pattern = "cutpointr", x = results$Solution)] <- "cutpointr" results$Solution[grep(pattern = "rocr", x = results$Solution)] <- "ROCR" results$Solution[grep(pattern = "optimal", x = results$Solution)] <- "OptimalCutpoints" results$Solution[grep(pattern = "proc", x = results$Solution)] <- "pROC" results$Solution[grep(pattern = "thres", x = results$Solution)] <- "ThresholdROC" results$task <- "Cutpoint Estimation" ``` ```{r, echo = FALSE} # These are the original results on our system # dput(results) results <- structure(list(time = c(4.5018015, 1.812802, 0.662101, 2.2887015, 1.194301, 4.839401, 2.1764015, 0.981001, 45.0568005, 36.2398515, 8.5662515, 5.667101, 2538.612001, 4.031701, 2503.8012505, 45.384501, 43.118751, 37.150151, 465.003201, 607.023851, 583.0950005, 5467.332801, 7850.2587, 7339.356101), Solution = c("cutpointr", "ROCR", "pROC", "OptimalCutpoints", "ThresholdROC", "cutpointr", "ROCR", "pROC", "OptimalCutpoints", "ThresholdROC", "cutpointr", "ROCR", "OptimalCutpoints", "pROC", "ThresholdROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC"), n = c(100, 100, 100, 100, 100, 1000, 1000, 1000, 1000, 1000, 10000, 10000, 10000, 10000, 10000, 1e+05, 1e+05, 1e+05, 1e+06, 1e+06, 1e+06, 1e+07, 1e+07, 1e+07), task = c("Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation", "Cutpoint Estimation")), row.names = c(NA, -24L), class = "data.frame") ``` ```{r, eval = FALSE} # ROCR package rocr_roc <- function(x, class) { pred <- ROCR::prediction(x, class) perf <- ROCR::performance(pred, "sens", "spec") return(NULL) } # pROC package proc_roc <- function(x, class) { r <- pROC::roc(class, x, algorithm = 2, levels = c(0, 1), direction = "<") return(NULL) } ``` ```{r, eval = FALSE, echo = FALSE} n <- 100 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_100 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 100, unit = "ms" ) n <- 1000 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1000 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 100, unit = "ms" ) n <- 10000 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_10000 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 100, unit = "ms" ) n <- 1e5 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e5 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 100, unit = "ms" ) n <- 1e6 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e6 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 30, unit = "ms" ) n <- 1e7 set.seed(123) dat <- data.frame(x = rnorm(n), y = sample(c(0:1), size = n, replace = TRUE)) bench_1e7 <- microbenchmark::microbenchmark( cutpointr::roc(dat, "x", "y", pos_class = 1, neg_class = 0, direction = ">="), rocr_roc(dat$x, dat$y), proc_roc(dat$x, dat$y), times = 30, unit = "ms" ) results_roc <- rbind( data.frame(time = summary(bench_100)$median, Solution = summary(bench_100)$expr, n = 100), data.frame(time = summary(bench_1000)$median, Solution = summary(bench_1000)$expr, n = 1000), data.frame(time = summary(bench_10000)$median, Solution = summary(bench_10000)$expr, n = 10000), data.frame(time = summary(bench_1e5)$median, Solution = summary(bench_1e5)$expr, n = 1e5), data.frame(time = summary(bench_1e6)$median, Solution = summary(bench_1e6)$expr, n = 1e6), data.frame(time = summary(bench_1e7)$median, Solution = summary(bench_1e7)$expr, n = 1e7) ) results_roc$Solution <- as.character(results_roc$Solution) results_roc$Solution[grep(pattern = "cutpointr", x = results_roc$Solution)] <- "cutpointr" results_roc$Solution[grep(pattern = "rocr", x = results_roc$Solution)] <- "ROCR" results_roc$Solution[grep(pattern = "proc", x = results_roc$Solution)] <- "pROC" results_roc$task <- "ROC curve calculation" ``` ```{r, echo = FALSE} # Our results results_roc <- structure(list(time = c(0.7973505, 1.732651, 0.447701, 0.859301, 2.0358515, 0.694802, 1.878151, 5.662151, 3.6580505, 11.099251, 42.8208515, 35.3293005, 159.8100505, 612.471901, 610.4337005, 2032.693551, 7806.3854515, 7081.897251), Solution = c("cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC", "cutpointr", "ROCR", "pROC"), n = c(100, 100, 100, 1000, 1000, 1000, 10000, 10000, 10000, 1e+05, 1e+05, 1e+05, 1e+06, 1e+06, 1e+06, 1e+07, 1e+07, 1e+07), task = c("ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation", "ROC curve calculation")), row.names = c(NA, -18L), class = "data.frame") ``` ```{r, echo = FALSE} library(ggplot2) results_all <- dplyr::bind_rows(results, results_roc) ggplot(results_all, aes(x = n, y = time, col = Solution, shape = Solution)) + geom_point(size = 3) + geom_line() + scale_y_log10(breaks = c(0.5, 1, 2, 3, 5, 10, 25, 100, 250, 1000, 5000, 1e4, 15000)) + scale_x_log10(breaks = c(100, 1000, 1e4, 1e5, 1e6, 1e7)) + ylab("Median Time (milliseconds, log scale)") + xlab("Sample Size (log scale)") + theme_bw() + theme(legend.position = "bottom", legend.key.width = unit(0.8, "cm"), panel.spacing = unit(1, "lines")) + facet_grid(~task) ``` ```{r, echo = FALSE} library(tidyr) res_table <- tidyr::spread(results_all, Solution, time) res_table <- dplyr::arrange(res_table, task) knitr::kable(res_table) ``` cutpointr/inst/doc/cutpointr_estimation.Rmd0000644000176200001440000002260214726126006020760 0ustar liggesusers--- title: "Robust cutpoint estimation" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Robust cutpoint estimation} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` # More robust cutpoint estimation methods ## Bootstrapped cutpoints It has been shown that bagging can substantially improve performance of a wide range of types of models in regression as well as in classification tasks. This method is available for cutpoint estimation via the `maximize_boot_metric` and `minimize_boot_metric` functions. If one of these functions is used as `method`, `boot_cut` bootstrap samples are drawn, the cutpoint optimization is carried out in each one and a summary (e.g. the mean) of the resulting optimal cutpoints on the bootstrap samples is returned as the optimal cutpoint in `cutpointr`. Note that if bootstrap validation is run, i.e. if `boot_runs` is larger zero, an outer bootstrap will be executed. In the bootstrap validation routine `boot_runs` bootstrap samples are generated and each one is again bootstrapped `boot_cut` times. This may lead to long run times, so activating the built-in parallelization may be advisable. The advantages of bootstrapping the optimal cutpoint are that the procedure doesn't possess parameters that have to be tuned, unlike the LOESS smoothing, that it doesn't rely on assumptions, unlike the Normal method, and that it is applicable to any metric that can be used with `minimize_metric` or `maximize_metric`, unlike the Kernel method. Furthermore, like Random Forests cannot be overfit by increasing the number of trees, the bootstrapped cutpoints cannot be overfit by running an excessive amount of `boot_cut` repetitions. ```{r, cache=TRUE} library(cutpointr) set.seed(100) cutpointr(suicide, dsi, suicide, gender, method = maximize_boot_metric, boot_cut = 200, summary_func = mean, metric = accuracy, silent = TRUE) ``` ## LOESS smoothing for selecting a cutpoint When using `maximize_metric` and `minimize_metric` the optimal cutpoint is selected by searching the maximum or minimum of the metric function. For example, we may want to minimize the misclassification cost. Since false negatives (a suicide attempt was not anticipated) can be regarded as much more severe than false positives we can set the cost of a false negative `cost_fn` for example to ten times the cost of a false positive. ```{r} opt_cut <- cutpointr(suicide, dsi, suicide, gender, method = minimize_metric, metric = misclassification_cost, cost_fp = 1, cost_fn = 10) ``` ```{r} plot_metric(opt_cut) ``` As this "optimal" cutpoint may depend on minor differences between the possible cutoffs, smoothing of the function of metric values by cutpoint value might be desirable, especially in small samples. The `minimize_loess_metric` and `maximize_loess_metric` functions can be used to smooth the function so that the optimal cutpoint is selected based on the smoothed metric values. Options to modify the smoothing, which is implemented using `loess.as` from the **fANCOVA** package, include: - `criterion`: the criterion for automatic smoothing parameter selection: "aicc" denotes bias-corrected AIC criterion, "gcv" denotes generalized cross-validation. - `degree`: the degree of the local polynomials to be used. It can be 0, 1 or 2. - `family`: if "gaussian" fitting is by least-squares, and if "symmetric" a re-descending M estimator is used with Tukey's biweight function. - `user.span`: the user-defined parameter which controls the degree of smoothing. Using parameters for the LOESS smoothing of `criterion = "aicc"`, `degree = 2`, `family = "symmetric"`, and `user.span = 0.7` we get the following smoothed versions of the above metrics: ```{r, message = FALSE} opt_cut <- cutpointr(suicide, dsi, suicide, gender, method = minimize_loess_metric, criterion = "aicc", family = "symmetric", degree = 2, user.span = 0.7, metric = misclassification_cost, cost_fp = 1, cost_fn = 10) ``` ```{r} plot_metric(opt_cut) ``` The optimal cutpoint for the female subgroup changes to 3. Note, though, that there are no reliable rules for selecting the "best" smoothing parameters. Notably, the LOESS smoothing is sensitive to the number of unique cutpoints. A large number of unique cutpoints generally leads to a more volatile curve of metric values by cutpoint value, even after smoothing. Thus, the curve tends to be undersmoothed in that scenario. The unsmoothed metric values are returned in `opt_cut$roc_curve` in the column `m_unsmoothed`. ## Smoothing via Generalized Additive Models for selecting a cutpoint In a similar fashion, the function of metric values per cutpoint can be smoothed using Generalized Additive Models with smooth terms. Internally, `mgcv::gam` carries out the smoothing which can be customized via the arguments `formula` and `optimizer`, see `help("gam", package = "mgcv")`. Most importantly, the GAM can be specified by altering the default formula, for example the smoothing function could be configured to apply cubic regression splines (`"cr"`) as the smooth term. As the `suicide` data has only very few unique cutpoints, it is not very suitable for showcasing the GAM smoothing, so we will use two classes of the `iris` data here. In this case, the purely empirical method and the GAM smoothing lead to identical cutpoints, but in practice the GAM smoothing tends to be more robust, especially with larger data. An attractive feature of the GAM smoothing is that the default values tend to work quite well and usually require no tuning, eliminating researcher degrees of freedom. ```{r} library(ggplot2) exdat <- iris exdat <- exdat[exdat$Species != "setosa", ] opt_cut <- cutpointr(exdat, Petal.Length, Species, method = minimize_gam_metric, formula = m ~ s(x.sorted, bs = "cr"), metric = abs_d_sens_spec) plot_metric(opt_cut) ``` ### Parametric method assuming normality The Normal method in `oc_youden_normal` is a parametric method for maximizing the Youden-Index or equivalently the sum of $Se$ and $Sp$. It relies on the assumption that the predictor for both the negative and positive observations is normally distributed. In that case it can be shown that $$c^* = \frac{(\mu_P \sigma_N^2 - \mu_N \sigma_P^2) - \sigma_N \sigma_P \sqrt{(\mu_N - \mu_P)^2 + (\sigma_N^2 - \sigma_P^2) log(\sigma_N^2 / \sigma_P^2)}}{\sigma_N^2 - \sigma_P^2}$$ where the negative class is normally distributed with $\sim N(\mu_N, \sigma_N^2)$ and the positive class independently normally distributed with $\sim N(\mu_P, \sigma_P^2)$ provides the optimal cutpoint $c^*$ that maximizes the Youden-Index. If $\sigma_N$ and $\sigma_P$ are equal, the expression can be simplified to $c^* = \frac{\mu_N + \mu_P}{2}$. However, the `oc_youden_normal` method in cutpointr always assumes unequal standard deviations. Since this method does not select a cutpoint from the observed predictor values, it is questionable which values for $Se$ and $Sp$ should be reported. Here, the Youden-Index can be calculated as $$J = \Phi(\frac{c^* - \mu_N}{\sigma_N}) - \Phi(\frac{c^* - \mu_P}{\sigma_P})$$ if the assumption of normality holds. However, since there exist several methods that do not select cutpoints from the available observations and to unify the reporting of metrics for these methods, **cutpointr** reports all metrics, e.g. $Se$ and $Sp$, based on the empirical observations. ```{r} cutpointr(suicide, dsi, suicide, gender, method = oc_youden_normal) ``` ### Nonparametric kernel method A nonparametric alternative is the Kernel method [@fluss_estimation_2005]. Here, the empirical distribution functions are smoothed using the Gaussian kernel functions $\hat{F}_N(t) = \frac{1}{n} \sum^n_{i=1} \Phi(\frac{t - y_i}{h_y})$ and $\hat{G}_P(t) = \frac{1}{m} \sum^m_{i=1} \Phi(\frac{t - x_i}{h_x})$ for the negative and positive classes respectively. Following Silverman's plug-in "rule of thumb" the bandwidths are selected as $h_y = 0.9 * min\{s_y, iqr_y/1.34\} * n^{-0.2}$ and $h_x = 0.9 * min\{s_x, iqr_x/1.34\} * m^{-0.2}$ where $s$ is the sample standard deviation and $iqr$ is the inter quartile range. It has been demonstrated that AUC estimation is rather insensitive to the choice of the bandwidth procedure [@faraggi_estimation_2002] and thus the plug-in bandwidth estimator has also been recommended for cutpoint estimation. The `oc_youden_kernel` function in **cutpointr** uses a Gaussian kernel and the direct plug-in method for selecting the bandwidths. The kernel smoothing is done via the `bkde` function from the **KernSmooth** package [@wand_kernsmooth:_2013]. Again, there is a way to calculate the Youden-Index from the results of this method [@fluss_estimation_2005] which is $$\hat{J} = max_c \{\hat{F}_N(c) - \hat{G}_N(c) \}$$ but as before we prefer to report all metrics based on applying the cutpoint that was estimated using the Kernel method to the empirical observations. ```{r} cutpointr(suicide, dsi, suicide, gender, method = oc_youden_kernel) ```cutpointr/inst/doc/cutpointr.R0000644000176200001440000003235514726136471016220 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ## ----CRAN, eval = FALSE------------------------------------------------------- # install.packages("cutpointr") ## ----------------------------------------------------------------------------- library(cutpointr) data(suicide) head(suicide) cp <- cutpointr(suicide, dsi, suicide, method = maximize_metric, metric = sum_sens_spec) ## ----------------------------------------------------------------------------- summary(cp) ## ----------------------------------------------------------------------------- plot(cp) ## ----------------------------------------------------------------------------- opt_cut <- cutpointr(suicide, dsi, suicide, direction = ">=", pos_class = "yes", neg_class = "no", method = maximize_metric, metric = youden) ## ----------------------------------------------------------------------------- plot_metric(opt_cut) ## ----------------------------------------------------------------------------- predict(opt_cut, newdata = data.frame(dsi = 0:5)) ## ----separate subgroups and bootstrapping, eval = FALSE----------------------- # set.seed(12) # opt_cut_b <- cutpointr(suicide, dsi, suicide, boot_runs = 1000) ## ----------------------------------------------------------------------------- opt_cut_b ## ----------------------------------------------------------------------------- opt_cut_b$boot ## ----------------------------------------------------------------------------- summary(opt_cut_b) plot(opt_cut_b) ## ----eval = FALSE------------------------------------------------------------- # library(doParallel) # cl <- makeCluster(2) # 2 cores # registerDoParallel(cl) # registerDoRNG(12) # Reproducible parallel loops using doRNG # opt_cut <- cutpointr(suicide, dsi, suicide, gender, pos_class = "yes", # direction = ">=", boot_runs = 1000, allowParallel = TRUE) # stopCluster(cl) # opt_cut ## ----------------------------------------------------------------------------- library(tidyr) library(dplyr) opt_cut <- cutpointr(suicide, dsi, suicide, metric = sum_sens_spec, tol_metric = 0.05, break_ties = c) opt_cut |> select(optimal_cutpoint, sum_sens_spec) |> unnest(cols = c(optimal_cutpoint, sum_sens_spec)) ## ----eval = FALSE------------------------------------------------------------- # set.seed(100) # opt_cut_manual <- cutpointr(suicide, dsi, suicide, method = oc_manual, # cutpoint = mean(suicide$dsi), boot_runs = 1000) # set.seed(100) # opt_cut_mean <- cutpointr(suicide, dsi, suicide, method = oc_mean, boot_runs = 1000) ## ----eval = FALSE------------------------------------------------------------- # myvar <- "dsi" # cutpointr(suicide, !!myvar, suicide) ## ----------------------------------------------------------------------------- dat <- data.frame(outcome = c("neg", "neg", "neg", "pos", "pos", "pos", "pos"), pred = c(1, 2, 3, 8, 11, 11, 12)) ## ----------------------------------------------------------------------------- opt_cut <- cutpointr(dat, x = pred, class = outcome, use_midpoints = TRUE) plot_x(opt_cut) ## ----echo = FALSE------------------------------------------------------------- plotdat_nomidpoints <- structure(list(sim_nr = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 36L), method = structure(c(1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L), levels = c("emp", "normal", "loess", "boot", "spline", "spline_20", "kernel", "gam"), class = "factor"), n = c(30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 750, 750, 1000, 1000, 1000, 1000, 1000, 1000, 30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 750, 750, 1000, 1000, 1000, 1000, 1000, 1000, 30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 750, 750, 1000, 1000, 1000, 1000, 1000, 1000, 30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 750, 750, 1000, 1000, 1000, 1000, 1000, 1000), mean_err = c(0.532157164015659, 0.0344907054484091, 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0.0221776555499961, 0.0389702052631117, -0.0016189234058987, 0.0197144417326668, -0.00632485262604172, -0.00364979854195596, -0.00276076468388984, 0.0126267527301874, -0.00591512196680815, 0.0098685016547149, 1.19276916750486, -0.0296831640401583, 0.99406393593888, 1.95758669445116, -0.0240222410217233, 1.00771193034927, 0.748151091428865, 0.001671855025917, 0.665180535306263, 1.1777049634557, 0.0292152981607387, 0.615230814912951, 0.417886753756131, 0.00324593885807739, 0.406076310942717, 0.732191741449251, -0.000759357576337989, 0.350075431324921, 0.310927617707656, -0.0107255472998434, 0.28102101085112, 0.514683023356017, -0.0220885572014814, 0.243370611433649, 0.209652330609093, -0.00502865663759991, 0.2172246261346, 0.356540958804122, 0.00365942442127361, 0.176594681455494, 0.126956927057327, -0.00270525933073803, 0.120116234221594, 0.210827536708082, 0.00238043252144371, 0.113027315928011, 0.0598624378953727, -0.00538838415690431, 0.0568400730102315, 0.0978115258288965, -0.00165813015281622, 0.0521772135812508, 0.0530224090961669, -0.000416993405198653, 0.0353236911458531, 0.0605493601241619, -0.00446984887315054, 0.0328807595695966, 0.0396438546423947, -0.00331466369719113, 0.0379029847219126, 0.0572435100638761, 0.00220241478536399, 0.0307132312422208), youden = c(0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), row.names = c(NA, -216L), groups = structure(list( sim_nr = 1:36, .rows = structure(list(1:6, 7:12, 13:18, 19:24, 25:30, 31:36, 37:42, 43:48, 49:54, 55:60, 61:66, 67:72, 73:78, 79:84, 85:90, 91:96, 97:102, 103:108, 109:114, 115:120, 121:126, 127:132, 133:138, 139:144, 145:150, 151:156, 157:162, 163:168, 169:174, 175:180, 181:186, 187:192, 193:198, 199:204, 205:210, 211:216), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", "list"))), row.names = c(NA, -36L), .drop = TRUE, class = c("tbl_df", "tbl", "data.frame"))) ## ----echo = FALSE------------------------------------------------------------- library(ggplot2) ggplot(plotdat_nomidpoints, aes(x = n, y = mean_err, color = method, shape = method)) + geom_line() + geom_point() + facet_wrap(~ youden, scales = "fixed") + scale_shape_manual(values = 1:nlevels(plotdat_nomidpoints$method)) + scale_x_log10(breaks = c(30, 50, 75, 100, 150, 250, 500, 750, 1000)) + ggtitle("Bias of all methods when use_midpoints = FALSE", "normally distributed data, 10000 repetitions of simulation") cutpointr/inst/doc/cutpointr_user_functions.R0000644000176200001440000000112214726136577021341 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ## ----eval = FALSE------------------------------------------------------------- # mean_cut <- function(data, x, ...) { # oc <- mean(data[[x]]) # return(data.frame(optimal_cutpoint = oc)) # } ## ----------------------------------------------------------------------------- library(cutpointr) misclassification_cost cutpointr/inst/doc/cutpointr_user_functions.html0000644000176200001440000003445314726136577022121 0ustar liggesusers User-defined functions for estimation methods and metrics

User-defined functions for estimation methods and metrics

Christian Thiele

2024-12-10

User-defined functions

method

User-defined functions can be supplied to method, which is the function that is responsible for returning the optimal cutpoint. To define a new method function, create a function that may take as input(s):

  • data: A data.frame or tbl_df
  • x: (character) The name of the predictor variable
  • class: (character) The name of the class variable
  • metric_func: A function for calculating a metric, e.g. accuracy. Note that the method function does not necessarily have to accept this argument
  • pos_class: The positive class
  • neg_class: The negative class
  • direction: ">=" if the positive class has higher x values, "<=" otherwise
  • tol_metric: (numeric) In the built-in methods, all cutpoints will be returned that lead to a metric value in the interval [m_max - tol_metric, m_max + tol_metric] where m_max is the maximum achievable metric value. This can be used to return multiple decent cutpoints and to avoid floating-point problems.
  • use_midpoints: (logical) In the built-in methods, if TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = “>”) or the next lowest observation (for direction = “<”) which avoids biasing the optimal cutpoint.
  • ...: Further arguments that are passed to metric or that can be captured inside of method

The function should return a data frame or tibble with one row, the column optimal_cutpoint, and an optional column with an arbitrary name with the metric value at the optimal cutpoint.

For example, a function for choosing the cutpoint as the mean of the independent variable could look like this:

mean_cut <- function(data, x, ...) {
    oc <- mean(data[[x]])
    return(data.frame(optimal_cutpoint = oc))
}

If a method function does not return a metric column, the default sum_sens_spec, the sum of sensitivity and specificity, is returned as the extra metric column in addition to accuracy, sensitivity and specificity.

Some method functions that make use of the additional arguments (that are captured by ...) are already included in cutpointr, see the list at the top. Since these functions are arguments to cutpointr their code can be accessed by simply typing their name, see for example oc_youden_normal.

metric

User defined metric functions can be used as well. They are mainly useful in conjunction with method = maximize_metric, method = minimize_metric, or one of the other minimization and maximization functions. In case of a different method function metric will only be used as the main out-of-bag metric when plotting the result. The metric function should accept the following inputs as vectors:

  • tp: Vector of true positives
  • fp: Vector of false positives
  • tn: Vector of true negatives
  • fn: Vector of false negatives
  • ...: Further arguments

The function should return a numeric vector, a matrix, or a data.frame with one column. If the column is named, the name will be included in the output and plots. Avoid using names that are identical to the column names that are by default returned by cutpointr, as such names will be prefixed by metric_ in the output. The inputs (tp, fp, tn, and fn) are vectors. The code of the included metric functions can be accessed by simply typing their name.

For example, this is the misclassification_cost metric function:

library(cutpointr)
misclassification_cost
## function (tp, fp, tn, fn, cost_fp = 1, cost_fn = 1, ...) 
## {
##     misclassification_cost <- cost_fp * fp + cost_fn * fn
##     misclassification_cost <- matrix(misclassification_cost, 
##         ncol = 1)
##     colnames(misclassification_cost) <- "misclassification_cost"
##     return(misclassification_cost)
## }
## <bytecode: 0x000002468fc47a90>
## <environment: namespace:cutpointr>
cutpointr/inst/doc/cutpointr_roc.R0000644000176200001440000000350714726136576017066 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ## ----fig.width=4, fig.height=3------------------------------------------------ library(cutpointr) roc_curve <- roc(data = suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") auc(roc_curve) head(roc_curve) plot_roc(roc_curve) ## ----------------------------------------------------------------------------- mcp <- multi_cutpointr(suicide, class = suicide, pos_class = "yes", use_midpoints = TRUE, silent = TRUE) summary(mcp) ## ----eval = FALSE, message = FALSE-------------------------------------------- # set.seed(123) # opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500) ## ----message = FALSE---------------------------------------------------------- library(dplyr) library(tidyr) opt_cut_b_g |> group_by(subgroup) |> select(subgroup, boot) |> unnest(cols = boot) |> summarise(sd_oc_boot = sd(optimal_cutpoint), m_oc_boot = mean(optimal_cutpoint), m_acc_oob = mean(acc_oob)) ## ----------------------------------------------------------------------------- cutpointr(suicide, dsi, suicide, gender, metric = youden, silent = TRUE) |> add_metric(list(ppv, npv)) |> select(subgroup, optimal_cutpoint, youden, ppv, npv) ## ----------------------------------------------------------------------------- roc(data = suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") |> add_metric(list(cohens_kappa, F1_score)) |> select(x.sorted, tp, fp, tn, fn, cohens_kappa, F1_score) |> head() cutpointr/inst/doc/cutpointr.html0000644000176200001440000030631714726136471016765 0ustar liggesusers An introduction to cutpointr

An introduction to cutpointr

Christian Thiele

2024-12-10

cutpointr is an R package for tidy calculation of “optimal” cutpoints. It supports several methods for calculating cutpoints and includes several metrics that can be maximized or minimized by selecting a cutpoint. Some of these methods are designed to be more robust than the simple empirical optimization of a metric. Additionally, cutpointr can automatically bootstrap the variability of the optimal cutpoints and return out-of-bag estimates of various performance metrics.

Installation

You can install cutpointr from CRAN using the menu in RStudio or simply:

install.packages("cutpointr")

Example

For example, the optimal cutpoint for the included data set is 2 when maximizing the sum of sensitivity and specificity.

library(cutpointr)
data(suicide)
head(suicide)
##   age gender dsi suicide
## 1  29 female   1      no
## 2  26   male   0      no
## 3  26 female   0      no
## 4  27 female   0      no
## 5  28 female   0      no
## 6  53   male   2      no
cp <- cutpointr(suicide, dsi, suicide, 
                method = maximize_metric, metric = sum_sens_spec)
## Assuming the positive class is yes
## Assuming the positive class has higher x values
summary(cp)
## Method: maximize_metric 
## Predictor: dsi 
## Outcome: suicide 
## Direction: >= 
## 
##     AUC   n n_pos n_neg
##  0.9238 532    36   496
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##                 2        1.7518 0.8647      0.8889      0.8629 32  4 68 428
## 
## Predictor summary: 
##     Data Min.   5% 1st Qu. Median      Mean 3rd Qu.  95% Max.       SD NAs
##  Overall    0 0.00       0      0 0.9210526       1 5.00   11 1.852714   0
##       no    0 0.00       0      0 0.6330645       0 4.00   10 1.412225   0
##      yes    0 0.75       4      5 4.8888889       6 9.25   11 2.549821   0
plot(cp)

When considering the optimality of a cutpoint, we can only make a judgement based on the sample at hand. Thus, the estimated cutpoint may not be optimal within the population or on unseen data, which is why we sometimes put the “optimal” in quotation marks.

cutpointr makes assumptions about the direction of the dependency between class and x, if direction and / or pos_class or neg_class are not specified. The same result as above can be achieved by manually defining direction and the positive / negative classes which is slightly faster, since the classes and direction don’t have to be determined:

opt_cut <- cutpointr(suicide, dsi, suicide, direction = ">=", pos_class = "yes",
                     neg_class = "no", method = maximize_metric, metric = youden)

opt_cut is a data frame that returns the input data and the ROC curve (and optionally the bootstrap results) in a nested tibble. Methods for summarizing and plotting the data and results are included (e.g. summary, plot, plot_roc, plot_metric)

To inspect the optimization, the function of metric values per cutpoint can be plotted using plot_metric, if an optimization function was used that returns a metric column in the roc_curve column. For example, the maximize_metric and minimize_metric functions do so:

plot_metric(opt_cut)

Predictions for new data can be made using predict:

predict(opt_cut, newdata = data.frame(dsi = 0:5))
## [1] "no"  "no"  "yes" "yes" "yes" "yes"

Features

  • Calculation of optimal cutpoints in binary classification tasks
  • Tidy output, integrates well with functions from the tidyverse
  • Functions for plotting ROC curves, metric distributions and more
  • Bootstrapping for simulating the cutpoint variability and for obtaining out-of-bag estimates of various metrics (as a form of internal validation) with optional parallelisation
  • Multiple methods for calculating cutpoints
  • Multiple metrics can be chosen for maximization / minimization
  • Tidyeval

Calculating cutpoints

Method functions for cutpoint estimation

The included methods for calculating cutpoints are:

  • maximize_metric: Maximize the metric function
  • minimize_metric: Minimize the metric function
  • maximize_loess_metric: Maximize the metric function after LOESS smoothing
  • minimize_loess_metric: Minimize the metric function after LOESS smoothing
  • maximize_gam_metric: Maximize the metric function after smoothing via Generalized Additive Models
  • minimize_gam_metric: Minimize the metric function after smoothing via Generalized Additive Models
  • maximize_boot_metric: Bootstrap the optimal cutpoint when maximizing a metric
  • minimize_boot_metric: Bootstrap the optimal cutpoint when minimizing a metric
  • oc_manual: Specify the cutoff value manually
  • oc_mean: Use the sample mean as the “optimal” cutpoint
  • oc_median: Use the sample median as the “optimal” cutpoint
  • oc_youden_kernel: Maximize the Youden-Index after kernel smoothing the distributions of the two classes
  • oc_youden_normal: Maximize the Youden-Index parametrically assuming normally distributed data in both classes

Metric functions

The included metrics to be used with the minimization and maximization methods are:

  • accuracy: Fraction correctly classified
  • abs_d_sens_spec: The absolute difference of sensitivity and specificity
  • abs_d_ppv_npv: The absolute difference between positive predictive value (PPV) and negative predictive value (NPV)
  • roc01: Distance to the point (0,1) on ROC space
  • cohens_kappa: Cohen’s Kappa
  • sum_sens_spec: sensitivity + specificity
  • sum_ppv_npv: The sum of positive predictive value (PPV) and negative predictive value (NPV)
  • prod_sens_spec: sensitivity * specificity
  • prod_ppv_npv: The product of positive predictive value (PPV) and negative predictive value (NPV)
  • youden: Youden- or J-Index = sensitivity + specificity - 1
  • odds_ratio: (Diagnostic) odds ratio
  • risk_ratio: risk ratio (relative risk)
  • p_chisquared: The p-value of a chi-squared test on the confusion matrix
  • cost_misclassification: The sum of the misclassification cost of false positives and false negatives. Additional arguments: cost_fp, cost_fn
  • total_utility: The total utility of true / false positives / negatives. Additional arguments: utility_tp, utility_tn, cost_fp, cost_fn
  • F1_score: The F1-score (2 * TP) / (2 * TP + FP + FN)
  • metric_constrain: Maximize a selected metric given a minimal value of another selected metric
  • sens_constrain: Maximize sensitivity given a minimal value of specificity
  • spec_constrain: Maximize specificity given a minimal value of sensitivity
  • acc_constrain: Maximize accuracy given a minimal value of sensitivity

Furthermore, the following functions are included which can be used as metric functions but are more useful for plotting purposes, for example in plot_cutpointr, or for defining new metric functions: tp, fp, tn, fn, tpr, fpr, tnr, fnr, false_omission_rate, false_discovery_rate, ppv, npv, precision, recall, sensitivity, and specificity.

The inputs to the arguments method and metric are functions so that user-defined functions can easily be supplied instead of the built-in ones.

Separate subgroups and bootstrapping

Cutpoints can be separately estimated on subgroups that are defined by a third variable, gender in this case. Additionally, if boot_runs is larger zero, cutpointr will carry out the usual cutpoint calculation on the full sample, just as before, and additionally on boot_runs bootstrap samples. This offers a way of gauging the out-of-sample performance of the cutpoint estimation method. If a subgroup is given, the bootstrapping is carried out separately for every subgroup which is also reflected in the plots and output.

set.seed(12)
opt_cut_b <- cutpointr(suicide, dsi, suicide, boot_runs = 1000)
opt_cut_b
## # A tibble: 1 × 16
##   direction optimal_cutpoint method          sum_sens_spec      acc sensitivity
##   <chr>                <dbl> <chr>                   <dbl>    <dbl>       <dbl>
## 1 >=                       2 maximize_metric       1.75179 0.864662    0.888889
##   specificity      AUC pos_class neg_class prevalence outcome predictor
##         <dbl>    <dbl> <fct>     <fct>          <dbl> <chr>   <chr>    
## 1    0.862903 0.923779 yes       no         0.0676692 suicide dsi      
##   data               roc_curve            boot                 
##   <list>             <list>               <list>               
## 1 <tibble [532 × 2]> <rc_ctpnt [13 × 10]> <tibble [1,000 × 23]>

The returned object has the additional column boot which is a nested tibble that includes the cutpoints per bootstrap sample along with the metric calculated using the function in metric and various default metrics. The metrics are suffixed by _b to indicate in-bag results or _oob to indicate out-of-bag results:

opt_cut_b$boot
## [[1]]
## # A tibble: 1,000 × 23
##    optimal_cutpoint AUC_b AUC_oob sum_sens_spec_b sum_sens_spec_oob acc_b
##               <dbl> <dbl>   <dbl>           <dbl>             <dbl> <dbl>
##  1                2 0.957   0.884            1.80              1.71 0.874
##  2                1 0.918   0.935            1.70              1.70 0.752
##  3                2 0.920   0.946            1.79              1.73 0.874
##  4                2 0.940   0.962            1.82              1.76 0.893
##  5                2 0.849   0.96             1.66              1.76 0.848
##  6                4 0.926   0.927            1.80              1.51 0.925
##  7                2 0.927   0.919            1.74              1.78 0.885
##  8                2 0.958   0.882            1.82              1.67 0.863
##  9                4 0.911   0.923            1.80              1.53 0.914
## 10                1 0.871   0.975            1.62              1.80 0.737
## # ℹ 990 more rows
## # ℹ 17 more variables: acc_oob <dbl>, sensitivity_b <dbl>,
## #   sensitivity_oob <dbl>, specificity_b <dbl>, specificity_oob <dbl>,
## #   cohens_kappa_b <dbl>, cohens_kappa_oob <dbl>, TP_b <dbl>, FP_b <dbl>,
## #   TN_b <int>, FN_b <int>, TP_oob <dbl>, FP_oob <dbl>, TN_oob <int>,
## #   FN_oob <int>, roc_curve_b <list>, roc_curve_oob <list>

The summary and plots include additional elements that summarize or display the bootstrap results:

summary(opt_cut_b)
## Method: maximize_metric 
## Predictor: dsi 
## Outcome: suicide 
## Direction: >= 
## Nr. of bootstraps: 1000 
## 
##     AUC   n n_pos n_neg
##  0.9238 532    36   496
## 
##  optimal_cutpoint sum_sens_spec    acc sensitivity specificity tp fn fp  tn
##                 2        1.7518 0.8647      0.8889      0.8629 32  4 68 428
## 
## Predictor summary: 
##     Data Min.   5% 1st Qu. Median      Mean 3rd Qu.  95% Max.       SD NAs
##  Overall    0 0.00       0      0 0.9210526       1 5.00   11 1.852714   0
##       no    0 0.00       0      0 0.6330645       0 4.00   10 1.412225   0
##      yes    0 0.75       4      5 4.8888889       6 9.25   11 2.549821   0
## 
## Bootstrap summary: 
##           Variable Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##   optimal_cutpoint 1.00 1.00    2.00   2.00 2.12    2.00 4.00 4.00 0.72   0
##              AUC_b 0.83 0.88    0.91   0.93 0.92    0.94 0.96 0.98 0.02   0
##            AUC_oob 0.82 0.86    0.90   0.92 0.92    0.95 0.97 1.00 0.03   0
##    sum_sens_spec_b 1.57 1.67    1.72   1.76 1.76    1.80 1.84 1.89 0.05   0
##  sum_sens_spec_oob 1.37 1.56    1.66   1.72 1.71    1.78 1.86 1.90 0.09   0
##              acc_b 0.73 0.77    0.85   0.87 0.86    0.88 0.91 0.94 0.04   0
##            acc_oob 0.72 0.77    0.85   0.86 0.86    0.88 0.90 0.93 0.04   0
##      sensitivity_b 0.72 0.81    0.86   0.90 0.90    0.94 0.98 1.00 0.05   0
##    sensitivity_oob 0.44 0.67    0.80   0.87 0.86    0.93 1.00 1.00 0.10   0
##      specificity_b 0.72 0.76    0.85   0.86 0.86    0.88 0.91 0.94 0.04   0
##    specificity_oob 0.69 0.76    0.84   0.86 0.86    0.88 0.91 0.94 0.04   0
##     cohens_kappa_b 0.16 0.27    0.37   0.42 0.41    0.46 0.52 0.66 0.07   0
##   cohens_kappa_oob 0.15 0.25    0.34   0.39 0.39    0.44 0.51 0.62 0.08   0
plot(opt_cut_b)

Parallelized bootstrapping

Using foreach and doRNG the bootstrapping can be parallelized easily. The doRNG package is being used to make the bootstrap sampling reproducible.

library(doParallel)
cl <- makeCluster(2) # 2 cores
registerDoParallel(cl)
registerDoRNG(12) # Reproducible parallel loops using doRNG
opt_cut <- cutpointr(suicide, dsi, suicide, gender, pos_class = "yes",
                     direction = ">=", boot_runs = 1000, allowParallel = TRUE)
stopCluster(cl)
opt_cut

Additional features

Finding all cutpoints with acceptable performance

By default, most packages only return the “best” cutpoint and disregard other cutpoints with quite similar performance, even if the performance differences are minuscule. cutpointr makes this process more explicit via the tol_metric argument. For example, if all cutpoints are of interest that achieve at least an accuracy within 0.05 of the optimally achievable accuracy, tol_metric can be set to 0.05 and also those cutpoints will be returned.

In the case of the suicide data and when maximizing the sum of sensitivity and specificity, empirically the cutpoints 2 and 3 lead to quite similar performances. If tol_metric is set to 0.05, both will be returned.

library(tidyr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
opt_cut <- cutpointr(suicide, dsi, suicide, metric = sum_sens_spec, 
                     tol_metric = 0.05, break_ties = c)
## Assuming the positive class is yes
## Assuming the positive class has higher x values
## Multiple optimal cutpoints found, applying break_ties.
opt_cut |> 
    select(optimal_cutpoint, sum_sens_spec) |> 
    unnest(cols = c(optimal_cutpoint, sum_sens_spec))
## # A tibble: 2 × 2
##   optimal_cutpoint sum_sens_spec
##              <dbl>         <dbl>
## 1                2          1.75
## 2                1          1.70

Manual and mean / median cutpoints

Using the oc_manual function the optimal cutpoint will not be determined based on, for example, a metric but is instead set manually using the cutpoint argument. This is useful for supplying and evaluating cutpoints that were found in the literature or in other external sources.

The oc_manual function could also be used to set the cutpoint to the sample mean using cutpoint = mean(data$x). However, this may introduce bias into the bootstrap validation procedure, since the actual mean of the population is not known and thus the mean to be used as the cutpoint should be automatically determined in every resample. To do so, the oc_mean and oc_median functions can be used.

set.seed(100)
opt_cut_manual <- cutpointr(suicide, dsi, suicide, method = oc_manual, 
                       cutpoint = mean(suicide$dsi), boot_runs = 1000)
set.seed(100)
opt_cut_mean <- cutpointr(suicide, dsi, suicide, method = oc_mean, boot_runs = 1000)

Nonstandard evaluation via tidyeval

The arguments to cutpointr do not need to be enclosed in quotes. This is possible thanks to nonstandard evaluation of the arguments, which are evaluated on data.

Functions that use nonstandard evaluation are often not suitable for programming with. The use of nonstandard evaluation may lead to scoping problems and subsequent obvious as well as possibly subtle errors. cutpointr uses tidyeval internally and accordingly the same rules as for programming with dplyr apply. Arguments can be unquoted with !!:

myvar <- "dsi"
cutpointr(suicide, !!myvar, suicide)

Midpoints

So far - which is the default in cutpointr - we have considered all unique values of the predictor as possible cutpoints. An alternative could be to use a sequence of equidistant values instead, for example in the case of the suicide data all integers in \([0, 10]\). However, with very sparse data and small intervals between the candidate cutpoints (i.e. a ‘dense’ sequence like seq(0, 10, by = 0.01)) this leads to the uninformative evaluation of large ranges of cutpoints that all result in the same metric value. A more elegant alternative, not only for the case of sparse data, that is supported by cutpointr is the use of a mean value of the optimal cutpoint and the next highest (if direction = ">=") or the next lowest (if direction = "<=") predictor value in the data. The result is an optimal cutpoint that is equal to the cutpoint that would be obtained using an infinitely dense sequence of candidate cutpoints and is thus usually more efficient computationally.

This behavior can be activated by setting use_midpoints = TRUE, which is the default. If we use this setting, we obtain an optimal cutpoint of 1.5 for the complete sample on the suicide data instead of 2 when maximizing the sum of sensitivity and specificity.

Assume the following small data set:

dat <- data.frame(outcome = c("neg", "neg", "neg", "pos", "pos", "pos", "pos"),
                  pred    = c(1, 2, 3, 8, 11, 11, 12))

Since the distance of the optimal cutpoint (8) to the next lowest observation (3) is rather large we arrive at a range of possible cutpoints that all maximize the metric. In the case of this kind of sparseness it might for example be desirable to classify a new observation with a predictor value of 4 as belonging to the negative class. If use_midpoints is set to TRUE, the mean of the optimal cutpoint and the next lowest observation is returned as the optimal cutpoint, if direction is >=. The mean of the optimal cutpoint and the next highest observation is returned as the optimal cutpoint, if direction = "<=".

opt_cut <- cutpointr(dat, x = pred, class = outcome, use_midpoints = TRUE)
## Assuming the positive class is pos
## Assuming the positive class has higher x values
plot_x(opt_cut)

A simulation demonstrates more clearly that setting use_midpoints = TRUE avoids biasing the cutpoints. To simulate the bias of the metric functions, the predictor values of both classes were drawn from normal distributions with constant standard deviations of 10, a constant mean of the negative class of 100 and higher mean values of the positive class that are selected in such a way that optimal Youden-Index values of 0.2, 0.4, 0.6, and 0.8 result in the population. Samples of 9 different sizes were drawn and the cutpoints that maximize the Youden-Index were estimated. The simulation was repeated 10000 times. As can be seen by the mean error, use_midpoints = TRUE eliminates the bias that is introduced by otherwise selecting the value of an observation as the optimal cutpoint. If direction = ">=", as in this case, the observation that represents the optimal cutpoint is the highest possible cutpoint that leads to the optimal metric value and thus the biases are positive. The methods oc_youden_normal and oc_youden_kernel are always unbiased, as they don’t select a cutpoint based on the ROC-curve or the function of metric values per cutpoint.

cutpointr/inst/doc/cutpointr_bootstrapping.R0000644000176200001440000000400614726136543021163 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) ## ----------------------------------------------------------------------------- library(cutpointr) data(suicide) opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_metric, metric = youden, pos_class = "yes", direction = ">=" ) summary(opt_cut) ## ----------------------------------------------------------------------------- set.seed(123) opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_boot_metric, boot_cut = 200, summary_func = mean, metric = youden, pos_class = "yes", direction = ">=" ) summary(opt_cut) ## ----------------------------------------------------------------------------- opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_metric, metric = youden, pos_class = "yes", direction = ">=", boot_runs = 100 ) ## ----------------------------------------------------------------------------- summary(opt_cut) opt_cut$boot[[1]] |> head() ## ----------------------------------------------------------------------------- library(doParallel) library(doRNG) cl <- makeCluster(2) # 2 cores registerDoParallel(cl) registerDoRNG(12) set.seed(123) opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_boot_metric, boot_cut = 200, summary_func = mean, metric = youden, pos_class = "yes", direction = ">=", boot_runs = 100, allowParallel = TRUE ) stopCluster(cl) ## ----------------------------------------------------------------------------- summary(opt_cut) opt_cut$boot[[1]] |> head() ## ----------------------------------------------------------------------------- plot(opt_cut) cutpointr/inst/doc/cutpointr_bootstrapping.Rmd0000644000176200001440000001725014726066534021513 0ustar liggesusers--- title: "cutpointr: Bootstrapping" author: "Christian Thiele, Lorenz A. Kapsner" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Bootstrapping with cutpointr} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console markdown: wrap: 80 --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) ``` Bootstrapping is implemented in **cutpointr** with two goals: 1. Determine optimal cutpoints with bootstrapping (as an alternative to determining them without bootstrapping) 2. Validate (any) cutpoint optimization with bootstrapping This vignette will briefly go through some examples for both approaches. # Determine optimal cutpoints ## Without bootstrapping: `maximize_metric` As a first basic example, the cutpoint optimization will be demonstrated without any bootstrapping by maximizing the Youden-Index. Using the method `maximize_metric`, this is performed on the full data set: ```{r} library(cutpointr) data(suicide) opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_metric, metric = youden, pos_class = "yes", direction = ">=" ) summary(opt_cut) ``` The fields in the resulting R object `opt_cut` are to be interpreted as follows: - `$optimal_cutpoint`: The optimal cutpoint determined by maximizing the Youden-Index on the full `suicide` dataset. - `$sensitivity`: The sensitivity when applying the cutpoint to the full dataset. - `$specificity`: The specificity when applying the cutpoint to the full dataset. - `$youden`: The maximal Youden-Index (= sensitivity + specificity - 1), determined by the optimization. ## Bootstrap cutpoints: `maximize_boot_metric` The determination of the optimal cutpoint can also be performed using bootstrapping. Therefore, the methods `maximize_boot_metric`/`minimize_boot_metric` need to be chosen. These functions provide further arguments that can be used to configure the bootstrapping. These arguments can be viewed with `help("maximize_boot_metric", "cutpointr")`. The most important arguments are: - `boot_cut`: The number of bootstrapping repetitions. - `boot_stratify`: If the bootstrap samples are drawn in both classes separately before combining them, keep the number of positives/negatives constant in every sample. - `summary_func`: The summary function to aggregate the optimal cutpoints from the bootstrapping to arrive at one final optimal cutpoint. The cutpoint is optimized in n=`boot_cut` bootstrap samples by maximizing/ minimizing the respective metric (e.g., the Youden-index in this example) in each of these bootstrap samples. Finally, the summary function is applied to aggregate the optimal cutpoints from the n=`boot_cut` bootstrap samples into one final 'optimal' cutpoint. ```{r} set.seed(123) opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_boot_metric, boot_cut = 200, summary_func = mean, metric = youden, pos_class = "yes", direction = ">=" ) summary(opt_cut) ``` The fields in the resulting R object `opt_cut` are to be interpreted as follows: - `$optimal_cutpoint`: The optimal cutpoint, which is the aggregated value (as defined with `summary_func`) over all n=`boot_cut` bootstrap samples. Please note that no uncertainty measure (standard deviation, 95%-CI, etc.) is available here (a bootstrap distribution of these cutpoints can be generated using outer bootstrapping with `boot_runs > 0` and `maximize_metric`, as explained below). - `$sensitivity`: The sensitivity when applying the optimal cutpoint to the full dataset. - `$specificity`: The specificity when applying the optimal cutpoint to the full dataset. - `$youden`: The Youden-Index when applying the optimal cutpoint to the full dataset. # Validate cutpoint optimization with bootstrapping Any chosen methods to find the optimal cutpoints can be subsequently validated with bootstrapping. This can easily be activated by setting the argument `boot_runs` \> 0. Please be aware that the first steps to calculate the optimal cutpoints with the specified method (as described above) will be performed in the very same manner as above, resulting in the same outputs as above (depending on the seed when bootstrapping cutpoints). However, the method to calculate the optimal cutpoints will then additionally be performed on n=`boot_runs` bootstrap samples. For each of these bootstrap samples, several metrics and performance measures are available from the resulting `$boot` object, both for the *in-bag* (suffix: '\_b') and the *out-of-bag* (suffix: '\_oob') bootstrap samples. Please note that the optimal cutpoint is determined on the in-bag samples only and then just applied to the out-of-bag samples for validation purposes, so its value is available only once in the `$boot` object without a suffix. ## `maximize_metric` ```{r} opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_metric, metric = youden, pos_class = "yes", direction = ">=", boot_runs = 100 ) ``` The interpretation of fields in the resulting R object `opt_cut` is the same as above. The results from the bootstrapping are available from `$boot`. ```{r} summary(opt_cut) opt_cut$boot[[1]] |> head() ``` ## `maximize_boot_metric` When bootstrapping cutpoints and also using the validation with bootstrapping, the optimal cutpoint will again first be determined as above in n=`boot_cut` bootstrap samples by maximizing/ minimizing the respective metric in each of these bootstrap samples and then by applying the summary function to aggregate the optimal cutpoints from the n=`boot_cut` bootstrap samples into one final 'optimal' cutpoint. Hence, using the same seeds here results in the same outputs as above, where no outer bootstrapping is applied. In the validation routine, the chosen cutpoint optimization is then repeated in each of the n=`boot_runs` (outer) bootstrap samples: the optimal cutpoint is determined in each bootstrap sample by optimizing the `metric` on n=`boot_cut` (inner) bootstrap samples and applying the `summary_func` to aggregate them into one value. Since the (inner) bootstrapping of optimal cutpoints is performed in each of the (outer) validation bootstrap samples, this can be computational very expensive and take some time to finish. Therefore, parallelization is implemented in `cutpointr` by just setting its argument `allowParallel = TRUE` and initializing a parallel environment. ```{r} library(doParallel) library(doRNG) cl <- makeCluster(2) # 2 cores registerDoParallel(cl) registerDoRNG(12) set.seed(123) opt_cut <- cutpointr( data = suicide, x = dsi, class = suicide, method = maximize_boot_metric, boot_cut = 200, summary_func = mean, metric = youden, pos_class = "yes", direction = ">=", boot_runs = 100, allowParallel = TRUE ) stopCluster(cl) ``` Again, the interpretation of fields in the resulting R object `opt_cut` is the same as above. The results from the bootstrapping are available from `$boot`. ```{r} summary(opt_cut) opt_cut$boot[[1]] |> head() ``` Some visualizations of the bootstrapping results are available with the `plot` function: ```{r} plot(opt_cut) ``` The two plots in the lower half can be generated separately with `plot_cut_boot(opt_cut)` and `plot_metric_boot(opt_cut)`.cutpointr/inst/doc/cutpointr.Rmd0000644000176200001440000006042014726126404016526 0ustar liggesusers--- title: "An introduction to cutpointr" author: "Christian Thiele" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{An introduction to cutpointr} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center") options(rmarkdown.html_vignette.check_title = FALSE) load("vignettedata/vignettedata.Rdata") ``` **cutpointr** is an R package for tidy calculation of "optimal" cutpoints. It supports several methods for calculating cutpoints and includes several metrics that can be maximized or minimized by selecting a cutpoint. Some of these methods are designed to be more robust than the simple empirical optimization of a metric. Additionally, **cutpointr** can automatically bootstrap the variability of the optimal cutpoints and return out-of-bag estimates of various performance metrics. ## Installation You can install **cutpointr** from CRAN using the menu in RStudio or simply: ```{r CRAN, eval = FALSE} install.packages("cutpointr") ``` ## Example For example, the optimal cutpoint for the included data set is 2 when maximizing the sum of sensitivity and specificity. ```{r} library(cutpointr) data(suicide) head(suicide) cp <- cutpointr(suicide, dsi, suicide, method = maximize_metric, metric = sum_sens_spec) ``` ```{r} summary(cp) ``` ```{r} plot(cp) ``` When considering the optimality of a cutpoint, we can only make a judgement based on the sample at hand. Thus, the estimated cutpoint may not be optimal within the population or on unseen data, which is why we sometimes put the "optimal" in quotation marks. `cutpointr` makes assumptions about the direction of the dependency between `class` and `x`, if `direction` and / or `pos_class` or `neg_class` are not specified. The same result as above can be achieved by manually defining `direction` and the positive / negative classes which is slightly faster, since the classes and direction don't have to be determined: ```{r} opt_cut <- cutpointr(suicide, dsi, suicide, direction = ">=", pos_class = "yes", neg_class = "no", method = maximize_metric, metric = youden) ``` `opt_cut` is a data frame that returns the input data and the ROC curve (and optionally the bootstrap results) in a nested tibble. Methods for summarizing and plotting the data and results are included (e.g. `summary`, `plot`, `plot_roc`, `plot_metric`) To inspect the optimization, the function of metric values per cutpoint can be plotted using `plot_metric`, if an optimization function was used that returns a metric column in the `roc_curve` column. For example, the `maximize_metric` and `minimize_metric` functions do so: ```{r} plot_metric(opt_cut) ``` Predictions for new data can be made using `predict`: ```{r} predict(opt_cut, newdata = data.frame(dsi = 0:5)) ``` ## Features - Calculation of optimal cutpoints in binary classification tasks - Tidy output, integrates well with functions from the tidyverse - Functions for plotting ROC curves, metric distributions and more - Bootstrapping for simulating the cutpoint variability and for obtaining out-of-bag estimates of various metrics (as a form of internal validation) with optional parallelisation - Multiple methods for calculating cutpoints - Multiple metrics can be chosen for maximization / minimization - Tidyeval # Calculating cutpoints ## Method functions for cutpoint estimation The included methods for calculating cutpoints are: - `maximize_metric`: Maximize the metric function - `minimize_metric`: Minimize the metric function - `maximize_loess_metric`: Maximize the metric function after LOESS smoothing - `minimize_loess_metric`: Minimize the metric function after LOESS smoothing - `maximize_gam_metric`: Maximize the metric function after smoothing via Generalized Additive Models - `minimize_gam_metric`: Minimize the metric function after smoothing via Generalized Additive Models - `maximize_boot_metric`: Bootstrap the optimal cutpoint when maximizing a metric - `minimize_boot_metric`: Bootstrap the optimal cutpoint when minimizing a metric - `oc_manual`: Specify the cutoff value manually - `oc_mean`: Use the sample mean as the "optimal" cutpoint - `oc_median`: Use the sample median as the "optimal" cutpoint - `oc_youden_kernel`: Maximize the Youden-Index after kernel smoothing the distributions of the two classes - `oc_youden_normal`: Maximize the Youden-Index parametrically assuming normally distributed data in both classes ## Metric functions The included metrics to be used with the minimization and maximization methods are: - `accuracy`: Fraction correctly classified - `abs_d_sens_spec`: The absolute difference of sensitivity and specificity - `abs_d_ppv_npv`: The absolute difference between positive predictive value (PPV) and negative predictive value (NPV) - `roc01`: Distance to the point (0,1) on ROC space - `cohens_kappa`: Cohen's Kappa - `sum_sens_spec`: sensitivity + specificity - `sum_ppv_npv`: The sum of positive predictive value (PPV) and negative predictive value (NPV) - `prod_sens_spec`: sensitivity * specificity - `prod_ppv_npv`: The product of positive predictive value (PPV) and negative predictive value (NPV) - `youden`: Youden- or J-Index = sensitivity + specificity - 1 - `odds_ratio`: (Diagnostic) odds ratio - `risk_ratio`: risk ratio (relative risk) - `p_chisquared`: The p-value of a chi-squared test on the confusion matrix - `cost_misclassification`: The sum of the misclassification cost of false positives and false negatives. Additional arguments: cost_fp, cost_fn - `total_utility`: The total utility of true / false positives / negatives. Additional arguments: utility_tp, utility_tn, cost_fp, cost_fn - `F1_score`: The F1-score (2 * TP) / (2 * TP + FP + FN) - `metric_constrain`: Maximize a selected metric given a minimal value of another selected metric - `sens_constrain`: Maximize sensitivity given a minimal value of specificity - `spec_constrain`: Maximize specificity given a minimal value of sensitivity - `acc_constrain`: Maximize accuracy given a minimal value of sensitivity Furthermore, the following functions are included which can be used as metric functions but are more useful for plotting purposes, for example in `plot_cutpointr`, or for defining new metric functions: `tp`, `fp`, `tn`, `fn`, `tpr`, `fpr`, `tnr`, `fnr`, `false_omission_rate`, `false_discovery_rate`, `ppv`, `npv`, `precision`, `recall`, `sensitivity`, and `specificity`. The inputs to the arguments `method` and `metric` are functions so that user-defined functions can easily be supplied instead of the built-in ones. ## Separate subgroups and bootstrapping Cutpoints can be separately estimated on subgroups that are defined by a third variable, `gender` in this case. Additionally, if `boot_runs` is larger zero, `cutpointr` will carry out the usual cutpoint calculation on the full sample, just as before, and additionally on `boot_runs` bootstrap samples. This offers a way of gauging the out-of-sample performance of the cutpoint estimation method. If a subgroup is given, the bootstrapping is carried out separately for every subgroup which is also reflected in the plots and output. ```{r separate subgroups and bootstrapping, eval = FALSE} set.seed(12) opt_cut_b <- cutpointr(suicide, dsi, suicide, boot_runs = 1000) ``` ```{r} opt_cut_b ``` The returned object has the additional column `boot` which is a nested tibble that includes the cutpoints per bootstrap sample along with the metric calculated using the function in `metric` and various default metrics. The metrics are suffixed by `_b` to indicate in-bag results or `_oob` to indicate out-of-bag results: ```{r} opt_cut_b$boot ``` The summary and plots include additional elements that summarize or display the bootstrap results: ```{r} summary(opt_cut_b) plot(opt_cut_b) ``` ### Parallelized bootstrapping Using `foreach` and `doRNG` the bootstrapping can be parallelized easily. The **doRNG** package is being used to make the bootstrap sampling reproducible. ```{r, eval = FALSE} library(doParallel) cl <- makeCluster(2) # 2 cores registerDoParallel(cl) registerDoRNG(12) # Reproducible parallel loops using doRNG opt_cut <- cutpointr(suicide, dsi, suicide, gender, pos_class = "yes", direction = ">=", boot_runs = 1000, allowParallel = TRUE) stopCluster(cl) opt_cut ``` # Additional features ## Finding all cutpoints with acceptable performance By default, most packages only return the "best" cutpoint and disregard other cutpoints with quite similar performance, even if the performance differences are minuscule. **cutpointr** makes this process more explicit via the `tol_metric` argument. For example, if all cutpoints are of interest that achieve at least an accuracy within `0.05` of the optimally achievable accuracy, `tol_metric` can be set to `0.05` and also those cutpoints will be returned. In the case of the `suicide` data and when maximizing the sum of sensitivity and specificity, empirically the cutpoints 2 and 3 lead to quite similar performances. If `tol_metric` is set to `0.05`, both will be returned. ```{r} library(tidyr) library(dplyr) opt_cut <- cutpointr(suicide, dsi, suicide, metric = sum_sens_spec, tol_metric = 0.05, break_ties = c) opt_cut |> select(optimal_cutpoint, sum_sens_spec) |> unnest(cols = c(optimal_cutpoint, sum_sens_spec)) ``` ## Manual and mean / median cutpoints Using the `oc_manual` function the optimal cutpoint will not be determined based on, for example, a metric but is instead set manually using the `cutpoint` argument. This is useful for supplying and evaluating cutpoints that were found in the literature or in other external sources. The `oc_manual` function could also be used to set the cutpoint to the sample mean using `cutpoint = mean(data$x)`. However, this may introduce bias into the bootstrap validation procedure, since the actual mean of the population is not known and thus the mean to be used as the cutpoint should be automatically determined in every resample. To do so, the `oc_mean` and `oc_median` functions can be used. ```{r, eval = FALSE} set.seed(100) opt_cut_manual <- cutpointr(suicide, dsi, suicide, method = oc_manual, cutpoint = mean(suicide$dsi), boot_runs = 1000) set.seed(100) opt_cut_mean <- cutpointr(suicide, dsi, suicide, method = oc_mean, boot_runs = 1000) ``` ## Nonstandard evaluation via tidyeval The arguments to `cutpointr` do not need to be enclosed in quotes. This is possible thanks to nonstandard evaluation of the arguments, which are evaluated on `data`. Functions that use nonstandard evaluation are often not suitable for programming with. The use of nonstandard evaluation may lead to scoping problems and subsequent obvious as well as possibly subtle errors. **cutpointr** uses tidyeval internally and accordingly the same rules as for programming with `dplyr` apply. Arguments can be unquoted with `!!`: ```{r, eval = FALSE} myvar <- "dsi" cutpointr(suicide, !!myvar, suicide) ``` ## Midpoints So far - which is the default in `cutpointr` - we have considered all unique values of the predictor as possible cutpoints. An alternative could be to use a sequence of equidistant values instead, for example in the case of the `suicide` data all integers in $[0, 10]$. However, with very sparse data and small intervals between the candidate cutpoints (i.e. a 'dense' sequence like `seq(0, 10, by = 0.01)`) this leads to the uninformative evaluation of large ranges of cutpoints that all result in the same metric value. A more elegant alternative, not only for the case of sparse data, that is supported by **cutpointr** is the use of a mean value of the optimal cutpoint and the next highest (if `direction = ">="`) or the next lowest (if `direction = "<="`) predictor value in the data. The result is an optimal cutpoint that is equal to the cutpoint that would be obtained using an infinitely dense sequence of candidate cutpoints and is thus usually more efficient computationally. This behavior can be activated by setting `use_midpoints = TRUE`, which is the default. If we use this setting, we obtain an optimal cutpoint of 1.5 for the complete sample on the `suicide` data instead of 2 when maximizing the sum of sensitivity and specificity. Assume the following small data set: ```{r} dat <- data.frame(outcome = c("neg", "neg", "neg", "pos", "pos", "pos", "pos"), pred = c(1, 2, 3, 8, 11, 11, 12)) ``` Since the distance of the optimal cutpoint (8) to the next lowest observation (3) is rather large we arrive at a range of possible cutpoints that all maximize the metric. In the case of this kind of sparseness it might for example be desirable to classify a new observation with a predictor value of 4 as belonging to the negative class. If `use_midpoints` is set to `TRUE`, the mean of the optimal cutpoint and the next lowest observation is returned as the optimal cutpoint, if direction is `>=`. The mean of the optimal cutpoint and the next highest observation is returned as the optimal cutpoint, if `direction = "<="`. ```{r} opt_cut <- cutpointr(dat, x = pred, class = outcome, use_midpoints = TRUE) plot_x(opt_cut) ``` A simulation demonstrates more clearly that setting `use_midpoints = TRUE` avoids biasing the cutpoints. To simulate the bias of the metric functions, the predictor values of both classes were drawn from normal distributions with constant standard deviations of 10, a constant mean of the negative class of 100 and higher mean values of the positive class that are selected in such a way that optimal Youden-Index values of 0.2, 0.4, 0.6, and 0.8 result in the population. Samples of 9 different sizes were drawn and the cutpoints that maximize the Youden-Index were estimated. The simulation was repeated 10000 times. As can be seen by the mean error, `use_midpoints = TRUE` eliminates the bias that is introduced by otherwise selecting the value of an observation as the optimal cutpoint. If `direction = ">="`, as in this case, the observation that represents the optimal cutpoint is the highest possible cutpoint that leads to the optimal metric value and thus the biases are positive. The methods `oc_youden_normal` and `oc_youden_kernel` are always unbiased, as they don't select a cutpoint based on the ROC-curve or the function of metric values per cutpoint. ```{r, echo = FALSE} plotdat_nomidpoints <- structure(list(sim_nr = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 36L), method = structure(c(1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L, 1L, 2L, 3L, 4L, 7L, 8L), levels = c("emp", "normal", "loess", "boot", "spline", "spline_20", "kernel", "gam"), class = "factor"), n = c(30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 750, 750, 1000, 1000, 1000, 1000, 1000, 1000, 30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 750, 750, 1000, 1000, 1000, 1000, 1000, 1000, 30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 75, 75, 75, 75, 75, 75, 100, 100, 100, 100, 100, 100, 150, 150, 150, 150, 150, 150, 250, 250, 250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 750, 750, 750, 750, 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97:102, 103:108, 109:114, 115:120, 121:126, 127:132, 133:138, 139:144, 145:150, 151:156, 157:162, 163:168, 169:174, 175:180, 181:186, 187:192, 193:198, 199:204, 205:210, 211:216), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", "list"))), row.names = c(NA, -36L), .drop = TRUE, class = c("tbl_df", "tbl", "data.frame"))) ``` ```{r, echo = FALSE} library(ggplot2) ggplot(plotdat_nomidpoints, aes(x = n, y = mean_err, color = method, shape = method)) + geom_line() + geom_point() + facet_wrap(~ youden, scales = "fixed") + scale_shape_manual(values = 1:nlevels(plotdat_nomidpoints$method)) + scale_x_log10(breaks = c(30, 50, 75, 100, 150, 250, 500, 750, 1000)) + ggtitle("Bias of all methods when use_midpoints = FALSE", "normally distributed data, 10000 repetitions of simulation") ``` cutpointr/inst/doc/cutpointr_bootstrapping.html0000644000176200001440000013643314726136544021741 0ustar liggesusers cutpointr: Bootstrapping

cutpointr: Bootstrapping

Christian Thiele, Lorenz A. Kapsner

2024-12-10

Bootstrapping is implemented in cutpointr with two goals:

  1. Determine optimal cutpoints with bootstrapping (as an alternative to determining them without bootstrapping)
  2. Validate (any) cutpoint optimization with bootstrapping

This vignette will briefly go through some examples for both approaches.

Determine optimal cutpoints

Without bootstrapping: maximize_metric

As a first basic example, the cutpoint optimization will be demonstrated without any bootstrapping by maximizing the Youden-Index. Using the method maximize_metric, this is performed on the full data set:

library(cutpointr)
data(suicide)
opt_cut <- cutpointr(
    data = suicide,
    x = dsi,
    class = suicide,
    method = maximize_metric,
    metric = youden,
    pos_class = "yes",
    direction = ">="
)
summary(opt_cut)
## Method: maximize_metric 
## Predictor: dsi 
## Outcome: suicide 
## Direction: >= 
## 
##     AUC   n n_pos n_neg
##  0.9238 532    36   496
## 
##  optimal_cutpoint youden    acc sensitivity specificity tp fn fp  tn
##                 2 0.7518 0.8647      0.8889      0.8629 32  4 68 428
## 
## Predictor summary: 
##     Data Min.   5% 1st Qu. Median      Mean 3rd Qu.  95% Max.       SD NAs
##  Overall    0 0.00       0      0 0.9210526       1 5.00   11 1.852714   0
##       no    0 0.00       0      0 0.6330645       0 4.00   10 1.412225   0
##      yes    0 0.75       4      5 4.8888889       6 9.25   11 2.549821   0

The fields in the resulting R object opt_cut are to be interpreted as follows:

  • $optimal_cutpoint: The optimal cutpoint determined by maximizing the Youden-Index on the full suicide dataset.
  • $sensitivity: The sensitivity when applying the cutpoint to the full dataset.
  • $specificity: The specificity when applying the cutpoint to the full dataset.
  • $youden: The maximal Youden-Index (= sensitivity + specificity - 1), determined by the optimization.

Bootstrap cutpoints: maximize_boot_metric

The determination of the optimal cutpoint can also be performed using bootstrapping. Therefore, the methods maximize_boot_metric/minimize_boot_metric need to be chosen. These functions provide further arguments that can be used to configure the bootstrapping. These arguments can be viewed with help("maximize_boot_metric", "cutpointr"). The most important arguments are:

  • boot_cut: The number of bootstrapping repetitions.
  • boot_stratify: If the bootstrap samples are drawn in both classes separately before combining them, keep the number of positives/negatives constant in every sample.
  • summary_func: The summary function to aggregate the optimal cutpoints from the bootstrapping to arrive at one final optimal cutpoint.

The cutpoint is optimized in n=boot_cut bootstrap samples by maximizing/ minimizing the respective metric (e.g., the Youden-index in this example) in each of these bootstrap samples. Finally, the summary function is applied to aggregate the optimal cutpoints from the n=boot_cut bootstrap samples into one final ‘optimal’ cutpoint.

set.seed(123)
opt_cut <- cutpointr(
    data = suicide,
    x = dsi,
    class = suicide,
    method = maximize_boot_metric,
    boot_cut = 200,
    summary_func = mean,
    metric = youden,
    pos_class = "yes",
    direction = ">="
)
summary(opt_cut)
## Method: maximize_boot_metric 
## Predictor: dsi 
## Outcome: suicide 
## Direction: >= 
## 
##     AUC   n n_pos n_neg
##  0.9238 532    36   496
## 
##  optimal_cutpoint youden    acc sensitivity specificity tp fn fp  tn
##             2.055 0.6927 0.8816      0.8056      0.8871 29  7 56 440
## 
## Predictor summary: 
##     Data Min.   5% 1st Qu. Median      Mean 3rd Qu.  95% Max.       SD NAs
##  Overall    0 0.00       0      0 0.9210526       1 5.00   11 1.852714   0
##       no    0 0.00       0      0 0.6330645       0 4.00   10 1.412225   0
##      yes    0 0.75       4      5 4.8888889       6 9.25   11 2.549821   0

The fields in the resulting R object opt_cut are to be interpreted as follows:

  • $optimal_cutpoint: The optimal cutpoint, which is the aggregated value (as defined with summary_func) over all n=boot_cut bootstrap samples. Please note that no uncertainty measure (standard deviation, 95%-CI, etc.) is available here (a bootstrap distribution of these cutpoints can be generated using outer bootstrapping with boot_runs > 0 and maximize_metric, as explained below).
  • $sensitivity: The sensitivity when applying the optimal cutpoint to the full dataset.
  • $specificity: The specificity when applying the optimal cutpoint to the full dataset.
  • $youden: The Youden-Index when applying the optimal cutpoint to the full dataset.

Validate cutpoint optimization with bootstrapping

Any chosen methods to find the optimal cutpoints can be subsequently validated with bootstrapping. This can easily be activated by setting the argument boot_runs > 0. Please be aware that the first steps to calculate the optimal cutpoints with the specified method (as described above) will be performed in the very same manner as above, resulting in the same outputs as above (depending on the seed when bootstrapping cutpoints).

However, the method to calculate the optimal cutpoints will then additionally be performed on n=boot_runs bootstrap samples. For each of these bootstrap samples, several metrics and performance measures are available from the resulting $boot object, both for the in-bag (suffix: ‘_b’) and the out-of-bag (suffix: ‘_oob’) bootstrap samples. Please note that the optimal cutpoint is determined on the in-bag samples only and then just applied to the out-of-bag samples for validation purposes, so its value is available only once in the $boot object without a suffix.

maximize_metric

opt_cut <- cutpointr(
    data = suicide,
    x = dsi,
    class = suicide,
    method = maximize_metric,
    metric = youden,
    pos_class = "yes",
    direction = ">=",
    boot_runs = 100
)
## Running bootstrap...

The interpretation of fields in the resulting R object opt_cut is the same as above. The results from the bootstrapping are available from $boot.

summary(opt_cut)
## Method: maximize_metric 
## Predictor: dsi 
## Outcome: suicide 
## Direction: >= 
## Nr. of bootstraps: 100 
## 
##     AUC   n n_pos n_neg
##  0.9238 532    36   496
## 
##  optimal_cutpoint youden    acc sensitivity specificity tp fn fp  tn
##                 2 0.7518 0.8647      0.8889      0.8629 32  4 68 428
## 
## Predictor summary: 
##     Data Min.   5% 1st Qu. Median      Mean 3rd Qu.  95% Max.       SD NAs
##  Overall    0 0.00       0      0 0.9210526       1 5.00   11 1.852714   0
##       no    0 0.00       0      0 0.6330645       0 4.00   10 1.412225   0
##      yes    0 0.75       4      5 4.8888889       6 9.25   11 2.549821   0
## 
## Bootstrap summary: 
##          Variable Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##  optimal_cutpoint 1.00 1.00    2.00   2.00 2.08    2.00 4.00 4.00 0.69   0
##             AUC_b 0.85 0.89    0.90   0.92 0.92    0.94 0.96 0.97 0.02   0
##           AUC_oob 0.82 0.86    0.91   0.93 0.92    0.95 0.97 0.98 0.04   0
##          youden_b 0.60 0.67    0.72   0.75 0.75    0.79 0.85 0.89 0.06   0
##        youden_oob 0.49 0.58    0.67   0.73 0.72    0.78 0.84 0.87 0.08   0
##             acc_b 0.74 0.77    0.86   0.87 0.86    0.88 0.91 0.92 0.04   0
##           acc_oob 0.74 0.77    0.84   0.86 0.86    0.88 0.90 0.92 0.04   0
##     sensitivity_b 0.76 0.82    0.86   0.89 0.90    0.93 0.97 1.00 0.05   0
##   sensitivity_oob 0.60 0.69    0.81   0.87 0.86    0.92 1.00 1.00 0.09   0
##     specificity_b 0.72 0.76    0.85   0.87 0.86    0.88 0.91 0.92 0.04   0
##   specificity_oob 0.73 0.76    0.84   0.86 0.86    0.88 0.91 0.93 0.04   0
##    cohens_kappa_b 0.19 0.25    0.38   0.43 0.41    0.46 0.52 0.56 0.07   0
##  cohens_kappa_oob 0.15 0.25    0.34   0.39 0.39    0.44 0.49 0.56 0.08   0
opt_cut$boot[[1]] |> 
  head()
## # A tibble: 6 × 23
##   optimal_cutpoint AUC_b AUC_oob youden_b youden_oob acc_b acc_oob sensitivity_b
##              <dbl> <dbl>   <dbl>    <dbl>      <dbl> <dbl>   <dbl>         <dbl>
## 1                2 0.891   0.95     0.732      0.698 0.852   0.863         0.882
## 2                1 0.912   0.969    0.705      0.753 0.774   0.769         0.943
## 3                2 0.902   0.934    0.718      0.780 0.842   0.918         0.879
## 4                2 0.892   0.961    0.662      0.808 0.842   0.880         0.818
## 5                2 0.893   0.966    0.701      0.818 0.850   0.909         0.851
## 6                2 0.941   0.909    0.788      0.755 0.891   0.843         0.897
## # ℹ 15 more variables: sensitivity_oob <dbl>, specificity_b <dbl>,
## #   specificity_oob <dbl>, cohens_kappa_b <dbl>, cohens_kappa_oob <dbl>,
## #   TP_b <dbl>, FP_b <dbl>, TN_b <int>, FN_b <int>, TP_oob <dbl>, FP_oob <dbl>,
## #   TN_oob <int>, FN_oob <int>, roc_curve_b <list>, roc_curve_oob <list>

maximize_boot_metric

When bootstrapping cutpoints and also using the validation with bootstrapping, the optimal cutpoint will again first be determined as above in n=boot_cut bootstrap samples by maximizing/ minimizing the respective metric in each of these bootstrap samples and then by applying the summary function to aggregate the optimal cutpoints from the n=boot_cut bootstrap samples into one final ‘optimal’ cutpoint. Hence, using the same seeds here results in the same outputs as above, where no outer bootstrapping is applied.

In the validation routine, the chosen cutpoint optimization is then repeated in each of the n=boot_runs (outer) bootstrap samples: the optimal cutpoint is determined in each bootstrap sample by optimizing the metric on n=boot_cut (inner) bootstrap samples and applying the summary_func to aggregate them into one value.

Since the (inner) bootstrapping of optimal cutpoints is performed in each of the (outer) validation bootstrap samples, this can be computational very expensive and take some time to finish. Therefore, parallelization is implemented in cutpointr by just setting its argument allowParallel = TRUE and initializing a parallel environment.

library(doParallel)
## Warning: package 'doParallel' was built under R version 4.4.2
## Loading required package: foreach
## Loading required package: iterators
## Loading required package: parallel
library(doRNG)
## Warning: package 'doRNG' was built under R version 4.4.2
## Loading required package: rngtools
## Warning: package 'rngtools' was built under R version 4.4.2
cl <- makeCluster(2) # 2 cores
registerDoParallel(cl)
registerDoRNG(12)
set.seed(123)
opt_cut <- cutpointr(
    data = suicide,
    x = dsi,
    class = suicide,
    method = maximize_boot_metric,
    boot_cut = 200,
    summary_func = mean,
    metric = youden,
    pos_class = "yes",
    direction = ">=",
    boot_runs = 100,
    allowParallel = TRUE
)
## Running bootstrap...
stopCluster(cl)

Again, the interpretation of fields in the resulting R object opt_cut is the same as above. The results from the bootstrapping are available from $boot.

summary(opt_cut)
## Method: maximize_boot_metric 
## Predictor: dsi 
## Outcome: suicide 
## Direction: >= 
## Nr. of bootstraps: 100 
## 
##     AUC   n n_pos n_neg
##  0.9238 532    36   496
## 
##  optimal_cutpoint youden    acc sensitivity specificity tp fn fp  tn
##             2.055 0.6927 0.8816      0.8056      0.8871 29  7 56 440
## 
## Predictor summary: 
##     Data Min.   5% 1st Qu. Median      Mean 3rd Qu.  95% Max.       SD NAs
##  Overall    0 0.00       0      0 0.9210526       1 5.00   11 1.852714   0
##       no    0 0.00       0      0 0.6330645       0 4.00   10 1.412225   0
##      yes    0 0.75       4      5 4.8888889       6 9.25   11 2.549821   0
## 
## Bootstrap summary: 
##          Variable Min.   5% 1st Qu. Median Mean 3rd Qu.  95% Max.   SD NAs
##  optimal_cutpoint 1.07 1.60    1.93   2.08 2.16    2.28 2.97 3.60 0.45   0
##             AUC_b 0.86 0.89    0.91   0.93 0.93    0.94 0.96 0.96 0.02   0
##           AUC_oob 0.84 0.88    0.90   0.92 0.92    0.95 0.97 0.98 0.03   0
##          youden_b 0.60 0.63    0.68   0.72 0.72    0.76 0.79 0.84 0.05   0
##        youden_oob 0.48 0.57    0.64   0.69 0.71    0.78 0.84 0.88 0.09   0
##             acc_b 0.83 0.85    0.87   0.88 0.88    0.89 0.91 0.93 0.02   0
##           acc_oob 0.83 0.84    0.86   0.88 0.88    0.89 0.91 0.92 0.02   0
##     sensitivity_b 0.71 0.75    0.80   0.83 0.84    0.87 0.91 0.94 0.05   0
##   sensitivity_oob 0.58 0.69    0.75   0.81 0.83    0.91 1.00 1.00 0.10   0
##     specificity_b 0.83 0.85    0.87   0.88 0.88    0.89 0.91 0.94 0.02   0
##   specificity_oob 0.82 0.83    0.87   0.88 0.88    0.90 0.92 0.93 0.03   0
##    cohens_kappa_b 0.32 0.33    0.38   0.42 0.43    0.47 0.54 0.59 0.06   0
##  cohens_kappa_oob 0.22 0.31    0.37   0.41 0.41    0.47 0.53 0.56 0.07   0
opt_cut$boot[[1]] |> 
  head()
## # A tibble: 6 × 23
##   optimal_cutpoint AUC_b AUC_oob youden_b youden_oob acc_b acc_oob sensitivity_b
##              <dbl> <dbl>   <dbl>    <dbl>      <dbl> <dbl>   <dbl>         <dbl>
## 1             2.34 0.931   0.950    0.729      0.613 0.870   0.892         0.857
## 2             2.58 0.948   0.896    0.725      0.628 0.883   0.867         0.839
## 3             1.70 0.939   0.889    0.784      0.711 0.872   0.846         0.914
## 4             1.95 0.894   0.962    0.680      0.844 0.861   0.851         0.816
## 5             2.03 0.906   0.963    0.692      0.786 0.885   0.872         0.8  
## 6             2.58 0.928   0.881    0.771      0.540 0.900   0.860         0.868
## # ℹ 15 more variables: sensitivity_oob <dbl>, specificity_b <dbl>,
## #   specificity_oob <dbl>, cohens_kappa_b <dbl>, cohens_kappa_oob <dbl>,
## #   TP_b <dbl>, FP_b <dbl>, TN_b <int>, FN_b <int>, TP_oob <dbl>, FP_oob <dbl>,
## #   TN_oob <int>, FN_oob <int>, roc_curve_b <list>, roc_curve_oob <list>

Some visualizations of the bootstrapping results are available with the plot function:

plot(opt_cut)

The two plots in the lower half can be generated separately with plot_cut_boot(opt_cut) and plot_metric_boot(opt_cut).

cutpointr/inst/doc/cutpointr_benchmarks.html0000644000176200001440000010440214726136472021152 0ustar liggesusers cutpointr benchmarks

cutpointr benchmarks

Christian Thiele

2024-12-10

To offer a comparison to established solutions, cutpointr will be benchmarked against optimal.cutpoints from the OptimalCutpoints package, ThresholdROC and custom functions based on the ROCR and pROC packages. By generating data of different sizes the benchmarks will offer a comparison of the scalability of the different solutions.

Using prediction and performance from the ROCR package and roc from the pROC package, we can write functions for computing the cutpoint that maximizes the sum of sensitivity and specificity. pROC has a built-in function to optimize a few metrics:

# Return cutpoint that maximizes the sum of sensitivity and specificiy
# ROCR package
rocr_sensspec <- function(x, class) {
    pred <- ROCR::prediction(x, class)
    perf <- ROCR::performance(pred, "sens", "spec")
    sens <- slot(perf, "y.values")[[1]]
    spec <- slot(perf, "x.values")[[1]]
    cut <- slot(perf, "alpha.values")[[1]]
    cut[which.max(sens + spec)]
}

# pROC package
proc_sensspec <- function(x, class) {
    r <- pROC::roc(class, x, algorithm = 2, levels = c(0, 1), direction = "<")
    pROC::coords(r, "best", ret="threshold", transpose = FALSE)[1]
}

The benchmarking will be carried out using the microbenchmark package and randomly generated data. The values of the x predictor variable are drawn from a normal distribution which leads to a lot more unique values than were encountered before in the suicide data. Accordingly, the search for an optimal cutpoint is much more demanding, if all possible cutpoints are evaluated.

Benchmarks are run for sample sizes of 100, 1000, 1e4, 1e5, 1e6, and 1e7. For low sample sizes cutpointr is slower than the other solutions. While this should be of low practical importance, cutpointr scales more favorably with increasing sample size. The speed disadvantage in small samples that leads to the lower limit of around 25ms is mainly due to the nesting of the original data and the results that makes the compact output of cutpointr possible. This observation is emphasized by the fact that cutpointr::roc is quite fast also in small samples. For sample sizes > 1e5 cutpointr is a little faster than the function based on ROCR and pROC. Both of these solutions are generally faster than OptimalCutpoints and ThresholdROC with the exception of small samples. OptimalCutpoints and ThresholdROC had to be excluded from benchmarks with more than 1e4 observations due to high memory requirements and/or excessive run times, rendering the use of these packages in larger samples impractical.

# ROCR package
rocr_roc <- function(x, class) {
    pred <- ROCR::prediction(x, class)
    perf <- ROCR::performance(pred, "sens", "spec")
    return(NULL)
}

# pROC package
proc_roc <- function(x, class) {
    r <- pROC::roc(class, x, algorithm = 2, levels = c(0, 1), direction = "<")
    return(NULL)
}

n task OptimalCutpoints ROCR ThresholdROC cutpointr pROC
1e+02 Cutpoint Estimation 2.288702 1.812802 1.194301 4.5018015 0.662101
1e+03 Cutpoint Estimation 45.056801 2.176401 36.239852 4.8394010 0.981001
1e+04 Cutpoint Estimation 2538.612001 5.667101 2503.801251 8.5662515 4.031701
1e+05 Cutpoint Estimation NA 43.118751 NA 45.3845010 37.150151
1e+06 Cutpoint Estimation NA 607.023851 NA 465.0032010 583.095000
1e+07 Cutpoint Estimation NA 7850.258700 NA 5467.3328010 7339.356101
1e+02 ROC curve calculation NA 1.732651 NA 0.7973505 0.447701
1e+03 ROC curve calculation NA 2.035852 NA 0.8593010 0.694802
1e+04 ROC curve calculation NA 5.662151 NA 1.8781510 3.658050
1e+05 ROC curve calculation NA 42.820852 NA 11.0992510 35.329301
1e+06 ROC curve calculation NA 612.471901 NA 159.8100505 610.433700
1e+07 ROC curve calculation NA 7806.385452 NA 2032.6935510 7081.897251
cutpointr/build/0000755000176200001440000000000014726136577013422 5ustar liggesuserscutpointr/build/vignette.rds0000644000176200001440000000064514726136577015766 0ustar liggesusersSMO0.0E0?0Gp !x#e\.m'"h}m}ԁFW]=hSpʔLblYr:#c$ޥ_98J*#T4Frg9D\bq ;@?RI4H:zԿtX,ߛ/#$lB@zcutpointr/man/0000755000176200001440000000000014726125624013065 5ustar liggesuserscutpointr/man/prod_sens_spec.Rd0000644000176200001440000000343514726125617016371 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{prod_sens_spec} \alias{prod_sens_spec} \title{Calculate the product of sensitivity and specificity} \usage{ prod_sens_spec(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the product of sensitivity and specificity from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr sensitivity = tp / (tp + fn) \cr specificity = tn / (tn + fp) \cr prod_sens_spec = sensitivity * specificity \cr } \examples{ prod_sens_spec(10, 5, 20, 10) prod_sens_spec(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/sensitivity.Rd0000644000176200001440000000302414726125616015746 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{sensitivity} \alias{sensitivity} \title{Calculate sensitivity} \usage{ sensitivity(tp, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate sensitivity from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr sensitivity = tp / (tp + fn) \cr } \examples{ sensitivity(10, 5, 20, 10) sensitivity(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/sum_ppv_npv.Rd0000644000176200001440000000343214726125620015726 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{sum_ppv_npv} \alias{sum_ppv_npv} \title{Calculate the sum of positive and negative predictive value} \usage{ sum_ppv_npv(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the sum of positive predictive value (PPV) and negative predictive value (NPV) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr ppv = tp / (tp + fp) \cr npv = tn / (tn + fn) \cr sum_ppv_npv = ppv + npv \cr } \examples{ sum_ppv_npv(10, 5, 20, 10) sum_ppv_npv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/print.cutpointr.Rd0000644000176200001440000000134414726066534016544 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/print.cutpointr.R \name{print.cutpointr} \alias{print.cutpointr} \title{Print cutpointr objects} \source{ Kirill Müller and Hadley Wickham (2017). tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble } \usage{ \method{print}{cutpointr}(x, width = 1000, n = 50, sigfig = 6, ...) } \arguments{ \item{x}{a cutpointr object.} \item{width}{width of output.} \item{n}{number of rows to print.} \item{sigfig}{Number of significant digits to print. Temporarily overrides options("pillar.sigfig").} \item{...}{further arguments.} } \description{ Prints the \code{cutpointr} object with full width like a \code{tbl_df}. } cutpointr/man/cohens_kappa.Rd0000644000176200001440000000371714726125620016013 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{cohens_kappa} \alias{cohens_kappa} \title{Calculate Cohen's Kappa} \usage{ cohens_kappa(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \value{ A numeric matrix with the column name "cohens_kappa". } \description{ Calculate the Kappa metric from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr mrg_a = ((tp + fn) * (tp + fp)) / (tp + fn + fp + tn) \cr mrg_b = ((fp + tn) * (fn + tn)) / (tp + fn + fp + tn) \cr expec_agree = (mrg_a + mrg_b) / (tp + fn + fp + tn) \cr obs_agree = (tp + tn) / (tp + fn + fp + tn) \cr cohens_kappa = (obs_agree - expec_agree) / (1 - expec_agree) \cr } \examples{ cohens_kappa(10, 5, 20, 10) cohens_kappa(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/plr.Rd0000644000176200001440000000331614726125620014150 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{plr} \alias{plr} \alias{nlr} \title{Calculate the positive or negative likelihood ratio} \usage{ plr(tp, fp, tn, fn, ...) nlr(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the positive or negative likelihood ratio from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr plr = tpr / fpr \cr nlr = fnr / tnr \cr } \examples{ plr(10, 5, 20, 10) plr(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/roc.Rd0000644000176200001440000000450314726066534014145 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/roc.R \name{roc} \alias{roc} \title{Calculate a ROC curve} \source{ Forked from the \pkg{ROCR} package } \usage{ roc(data, x, class, pos_class, neg_class, direction = ">=", silent = FALSE) } \arguments{ \item{data}{A data.frame or matrix. Will be converted to a data.frame.} \item{x}{The name of the numeric predictor variable.} \item{class}{The name of the binary outcome variable.} \item{pos_class}{The value of 'class' that represents the positive cases.} \item{neg_class}{The value of 'class' that represents the negative cases.} \item{direction}{(character) One of ">=" or "<=". Specifies if the positive class is associated with higher values of x (default).} \item{silent}{If FALSE and the ROC curve contains no positives or negatives, a warning is generated.} } \value{ A data frame with the columns x.sorted, tp, fp, tn, fn, tpr, tnr, fpr, and fnr. } \description{ Given a \code{data.frame} with a numeric predictor variable and a binary outcome variable this function returns a \code{data.frame} that includes all elements of the confusion matrix (true positives, false positives, true negatives, and false negatives) for every unique value of the predictor variable. Additionally, the true positive rate (tpr), false positive rate (fpr), true negative rate (tnr) and false negative rate (fnr) are returned. } \details{ To enable classifying all observations as belonging to only one class the predictor values will be augmented by Inf or -Inf. The returned object can be plotted with plot_roc. This function uses tidyeval to support unquoted arguments. For programming with \code{roc} the operator \code{!!} can be used to unquote an argument, see the examples. } \examples{ roc_curve <- roc(data = suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") roc_curve plot_roc(roc_curve) auc(roc_curve) ## Unquoting an argument myvar <- "dsi" roc(suicide, x = !!myvar, suicide, pos_class = "yes", neg_class = "no") } \seealso{ Other main cutpointr functions: \code{\link{add_metric}()}, \code{\link{boot_ci}()}, \code{\link{boot_test}()}, \code{\link{cutpointr}()}, \code{\link{multi_cutpointr}()}, \code{\link{predict.cutpointr}()} } \concept{main cutpointr functions} cutpointr/man/maximize_metric.Rd0000644000176200001440000000637114726125622016547 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/optimize_metric.R \name{maximize_metric} \alias{maximize_metric} \alias{minimize_metric} \title{Optimize a metric function in binary classification} \usage{ maximize_metric( data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, tol_metric, use_midpoints, ... ) minimize_metric( data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, tol_metric, use_midpoints, ... ) } \arguments{ \item{data}{A data frame or tibble in which the columns that are given in x and class can be found.} \item{x}{(character) The variable name to be used for classification, e.g. predictions or test values.} \item{class}{(character) The variable name indicating class membership.} \item{metric_func}{(function) A function that computes a metric to be maximized. See description.} \item{pos_class}{The value of class that indicates the positive class.} \item{neg_class}{The value of class that indicates the negative class.} \item{direction}{(character) Use ">=" or "<=" to select whether an x value >= or <= the cutoff predicts the positive class.} \item{tol_metric}{All cutpoints will be returned that lead to a metric value in the interval [m_max - tol_metric, m_max + tol_metric] where m_max is the maximum achievable metric value. This can be used to return multiple decent cutpoints and to avoid floating-point problems.} \item{use_midpoints}{(logical) If TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = ">") or the next lowest observation (for direction = "<") which avoids biasing the optimal cutpoint.} \item{...}{Further arguments that will be passed to \code{metric_func}.} } \value{ A tibble with the columns \code{optimal_cutpoint}, the corresponding metric value and \code{roc_curve}, a nested tibble that includes all possible cutoffs and the corresponding numbers of true and false positives / negatives and all corresponding metric values. } \description{ Given a function for computing a metric in \code{metric_func}, these functions maximize or minimize that metric by selecting an optimal cutpoint. The metric function should accept the following inputs: \itemize{ \item \code{tp}: vector of number of true positives \item \code{fp}: vector of number of false positives \item \code{tn}: vector of number of true negatives \item \code{fn}: vector of number of false negatives } } \details{ The above inputs are arrived at by using all unique values in \code{x}, Inf, or -Inf as possible cutpoints for classifying the variable in class. } \examples{ cutpointr(suicide, dsi, suicide, method = maximize_metric, metric = accuracy) cutpointr(suicide, dsi, suicide, method = minimize_metric, metric = abs_d_sens_spec) } \seealso{ Other method functions: \code{\link{maximize_boot_metric}()}, \code{\link{maximize_gam_metric}()}, \code{\link{maximize_loess_metric}()}, \code{\link{oc_manual}()}, \code{\link{oc_mean}()}, \code{\link{oc_median}()}, \code{\link{oc_youden_kernel}()}, \code{\link{oc_youden_normal}()} } \concept{method functions} cutpointr/man/risk_ratio.Rd0000644000176200001440000000327214726125621015523 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{risk_ratio} \alias{risk_ratio} \title{Calculate the risk ratio (relative risk)} \usage{ risk_ratio(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the risk ratio (or relative risk) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr risk_ratio = (tp / (tp + fn)) / (fp / (fp + tn)) \cr } \examples{ risk_ratio(10, 5, 20, 10) risk_ratio(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/plot_cutpointr.Rd0000644000176200001440000000545514726125623016451 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_cutpointr.R \name{plot_cutpointr} \alias{plot_cutpointr} \title{General purpose plotting function for cutpointr or roc_cutpointr objects} \usage{ plot_cutpointr( x, xvar = cutpoint, yvar = sum_sens_spec, conf_lvl = 0.95, aspect_ratio = NULL ) } \arguments{ \item{x}{A \code{cutpointr} or \code{roc_cutpointr} object.} \item{xvar}{A function, typically \code{cutpoint} or a metric function.} \item{yvar}{A function, typically a metric function.} \item{conf_lvl}{(numeric) If bootstrapping was run and x is a cutpointr object, a confidence interval at the level of conf_lvl can be plotted. To plot no confidence interval set conf_lvl = 0.} \item{aspect_ratio}{(numeric) Set to 1 to obtain a quadratic plot, e.g. for plotting a ROC curve.} } \description{ Flexibly plot various metrics against all cutpoints or any other metric. The function can plot any metric based on a \code{cutpointr} or \code{roc_cutpointr} object. If \code{cutpointr} was run with bootstrapping, bootstrapped confidence intervals can be plotted. These represent the quantiles of the distribution of the y-variable grouped by x-variable over all bootstrap repetitions. } \details{ The arguments to \code{xvar} and \code{yvar} should be metric functions. Any metric function that is suitable for \code{cutpointr} can also be used in \code{plot_cutpointr}. Anonymous functions are also allowed. To plot all possible cutpoints, the utility function \code{cutpoint} can be used. The functions for \code{xvar} and \code{yvar} may accept any or all of the arguments \code{tp}, \code{fp}, \code{tn}, or \code{fn} and return a numeric vector, a matrix or a \code{data.frame}. For more details on metric functions see \code{vignette("cutpointr")}. Note that confidence intervals can only be correctly plotted if the values of \code{xvar} are constant across bootstrap samples. For example, confidence intervals for \code{tpr} by \code{fpr} (a ROC curve) cannot be plotted, as the values of the false positive rate vary per bootstrap sample. } \examples{ set.seed(1) oc <- cutpointr(suicide, dsi, suicide, boot_runs = 10) plot_cutpointr(oc, cutpoint, F1_score) ## ROC curve plot_cutpointr(oc, fpr, tpr, aspect_ratio = 1) ## Custom function plot_cutpointr(oc, cutpoint, function(tp, tn, fp, fn, ...) tp / fp) + ggplot2::ggtitle("Custom metric") + ggplot2::ylab("value") } \seealso{ Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cut_boot}()}, \code{\link{plot_metric}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_roc}()}, \code{\link{plot_sensitivity_specificity}()}, \code{\link{plot_x}()} } \concept{cutpointr plotting functions} cutpointr/man/plot.cutpointr.Rd0000644000176200001440000000202214726125623016353 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.cutpointr.R \name{plot.cutpointr} \alias{plot.cutpointr} \title{Plot cutpointr objects} \usage{ \method{plot}{cutpointr}(x, ...) } \arguments{ \item{x}{A cutpointr object.} \item{...}{Further arguments.} } \description{ The plot layout depends on whether subgroups were defined and whether bootstrapping was run. } \details{ The \code{...} argument can be used to apply \pkg{ggplot2} functions to every individual plot, for example for changing the theme. } \examples{ opt_cut <- cutpointr(suicide, dsi, suicide, gender) plot(opt_cut) plot(opt_cut, ggplot2::theme_bw()) } \seealso{ Other cutpointr plotting functions: \code{\link{plot_cut_boot}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_roc}()}, \code{\link{plot_sensitivity_specificity}()}, \code{\link{plot_x}()} } \concept{cutpointr plotting functions} cutpointr/man/oc_youden_kernel.Rd0000644000176200001440000000545214726125622016704 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/oc_youden_kernel.R \name{oc_youden_kernel} \alias{oc_youden_kernel} \title{Determine an optimal cutpoint maximizing the Youden-Index based on kernel smoothed densities} \source{ Fluss, R., Faraggi, D., & Reiser, B. (2005). Estimation of the Youden Index and its associated cutoff point. Biometrical Journal, 47(4), 458–472. Matt Wand (2015). KernSmooth: Functions for Kernel Smoothing Supporting Wand & Jones (1995). R package version 2.23-15. https://CRAN.R-project.org/package=KernSmooth } \usage{ oc_youden_kernel(data, x, class, pos_class, neg_class, direction, ...) } \arguments{ \item{data}{A data frame or tibble in which the columns that are given in x and class can be found.} \item{x}{(character) The variable name to be used for classification, e.g. predictions or test values.} \item{class}{(character) The variable name indicating class membership.} \item{pos_class}{The value of class that indicates the positive class.} \item{neg_class}{The value of class that indicates the negative class.} \item{direction}{(character) Use ">=" or "<=" to select whether an x value >= or <= the cutoff predicts the positive class.} \item{...}{To capture further arguments that are always passed to the method function by cutpointr. The cutpointr function passes data, x, class, metric_func, direction, pos_class and neg_class to the method function.} } \description{ Instead of searching for an optimal cutpoint to maximize (sensitivity + specificity - 1) on the ROC curve, this function first smoothes the empirical distributions of \code{x} per class. The smoothing is done using a binned kernel density estimate. The bandwidth is automatically selected using the direct plug-in method. } \details{ The functions for calculating the kernel density estimate and the bandwidth are both from \pkg{KernSmooth} with default parameters, except for the bandwidth selection, which uses the standard deviation as scale estimate. The cutpoint is estimated as the cutpoint that maximizes the Youden-Index given by \eqn{J = max_c {F_N(c) - G_N(c) }} where \eqn{J} and \eqn{G} are the smoothed distribution functions. } \examples{ data(suicide) if (require(KernSmooth)) { oc_youden_kernel(suicide, "dsi", "suicide", oc_metric = "Youden", pos_class = "yes", neg_class = "no", direction = ">=") ## Within cutpointr cutpointr(suicide, dsi, suicide, method = oc_youden_kernel) } } \seealso{ Other method functions: \code{\link{maximize_boot_metric}()}, \code{\link{maximize_gam_metric}()}, \code{\link{maximize_loess_metric}()}, \code{\link{maximize_metric}()}, \code{\link{oc_manual}()}, \code{\link{oc_mean}()}, \code{\link{oc_median}()}, \code{\link{oc_youden_normal}()} } \concept{method functions} cutpointr/man/cutpoint.Rd0000644000176200001440000000303014726125616015216 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{cutpoint} \alias{cutpoint} \alias{cutpoints} \title{Extract the cutpoints from a ROC curve generated by cutpointr} \usage{ cutpoint(x, ...) cutpoints(x, ...) } \arguments{ \item{x}{A roc_cutpointr object.} \item{...}{Further arguments.} } \description{ This is a utility function for extracting the cutpoints from a \code{roc_cutpointr} object. Mainly useful in conjunction with the \code{plot_cutpointr} function if cutpoints are to be plotted on the x-axis. } \examples{ oc <- cutpointr(suicide, dsi, suicide, gender) plot_cutpointr(oc, cutpoint, accuracy) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/maximize_boot_metric.Rd0000644000176200001440000001057214726125623017571 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/optimize_metric.R \name{maximize_boot_metric} \alias{maximize_boot_metric} \alias{minimize_boot_metric} \title{Optimize a metric function in binary classification after bootstrapping} \usage{ maximize_boot_metric( data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, summary_func = mean, boot_cut = 50, boot_stratify, inf_rm = TRUE, tol_metric, use_midpoints, ... ) minimize_boot_metric( data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, summary_func = mean, boot_cut = 50, boot_stratify, inf_rm = TRUE, tol_metric, use_midpoints, ... ) } \arguments{ \item{data}{A data frame or tibble in which the columns that are given in x and class can be found.} \item{x}{(character) The variable name to be used for classification, e.g. predictions or test values.} \item{class}{(character) The variable name indicating class membership.} \item{metric_func}{(function) A function that computes a single number metric to be maximized. See description.} \item{pos_class}{The value of class that indicates the positive class.} \item{neg_class}{The value of class that indicates the negative class.} \item{direction}{(character) Use ">=" or "<=" to select whether an x value >= or <= the cutoff predicts the positive class.} \item{summary_func}{(function) After obtaining the bootstrapped optimal cutpoints this function, e.g. mean or median, is applied to arrive at a single cutpoint.} \item{boot_cut}{(numeric) Number of bootstrap repetitions over which the mean optimal cutpoint is calculated.} \item{boot_stratify}{(logical) If the bootstrap is stratified, bootstrap samples are drawn in both classes and then combined, keeping the number of positives and negatives constant in every resample.} \item{inf_rm}{(logical) whether to remove infinite cutpoints before calculating the summary.} \item{tol_metric}{All cutpoints will be passed to \code{summary_func} that lead to a metric value in the interval [m_max - tol_metric, m_max + tol_metric] where m_max is the maximum achievable metric value. This can be used to return multiple decent cutpoints and to avoid floating-point problems.} \item{use_midpoints}{(logical) If TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = ">") or the next lowest observation (for direction = "<") which avoids biasing the optimal cutpoint.} \item{...}{To capture further arguments that are always passed to the method function by cutpointr. The cutpointr function passes data, x, class, metric_func, direction, pos_class and neg_class to the method function.} } \value{ A tibble with the column \code{optimal_cutpoint} } \description{ Given a function for computing a metric in \code{metric_func}, these functions bootstrap the data \code{boot_cut} times and maximize or minimize the metric by selecting an optimal cutpoint. The returned optimal cutpoint is the result of applying \code{summary_func}, e.g. the mean, to all optimal cutpoints that were determined in the bootstrap samples. The \code{metric} function should accept the following inputs: \itemize{ \item \code{tp}: vector of number of true positives \item \code{fp}: vector of number of false positives \item \code{tn}: vector of number of true negatives \item \code{fn}: vector of number of false negatives } } \details{ The above inputs are arrived at by using all unique values in \code{x}, Inf, and -Inf as possible cutpoints for classifying the variable in class. The reported metric represents the usual in-sample performance of the determined cutpoint. } \examples{ set.seed(100) cutpointr(suicide, dsi, suicide, method = maximize_boot_metric, metric = accuracy, boot_cut = 30) set.seed(100) cutpointr(suicide, dsi, suicide, method = minimize_boot_metric, metric = abs_d_sens_spec, boot_cut = 30) } \seealso{ Other method functions: \code{\link{maximize_gam_metric}()}, \code{\link{maximize_loess_metric}()}, \code{\link{maximize_metric}()}, \code{\link{oc_manual}()}, \code{\link{oc_mean}()}, \code{\link{oc_median}()}, \code{\link{oc_youden_kernel}()}, \code{\link{oc_youden_normal}()} } \concept{method functions} cutpointr/man/boot_test.Rd0000644000176200001440000000701414726066534015364 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/boot_test.R \name{boot_test} \alias{boot_test} \title{Test for equivalence of a metric} \source{ Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12(1), 77. https://doi.org/10.1186/1471-2105-12-77 } \usage{ boot_test(x, y = NULL, variable = "AUC", in_bag = TRUE, correction = "holm") } \arguments{ \item{x}{A cutpointr object with bootstrap results} \item{y}{If x does not contain subgroups another cutpointr object} \item{variable}{The variable for testing} \item{in_bag}{Whether the in-bag or out-of-bag results should be used for testing} \item{correction}{The type of correction for multiple testing. Possible values are as in p.adjust.methods} } \value{ A data.frame (a tibble) with the columns test_var, p, d, sd_d, z and in_bag. If a grouped cutpointr object was tested, the additional columns subgroup1, subgroup2 and p_adj are returned. } \description{ This function performs a significance test based on the bootstrap results of cutpointr to test whether a chosen metric is equal between subgroups or between two cutpointr objects. The test statistic is calculated as the standardized difference of the metric between groups. If \code{x} contains subgroups, the test is run on all possible pairings of subgroups. An additional adjusted p-value is returned in that case. } \details{ The variable name is looked up in the columns of the bootstrap results where the suffixes _b and _oob indicate in-bag and out-of-bag estimates, respectively (controlled via the \code{in_bag} argument). Possible values are optimal_cutpoint, AUC, acc, sensitivity, specificity, and the metric that was selected in \code{cutpointr}. Note that there is no "out-of-bag optimal cutpoint", so when selecting \code{variable = optimal_cutpoint} the test will be based on the in-bag data. The test statistic is calculated as z = (t1 - t2) / sd(t1 - t2) where t1 and t2 are the metric values on the full sample and sd(t1 - t2) is the standard deviation of the differences of the metric values per bootstrap repetition. The test is two-sided. If two cutpointr objects are compared and the numbers of bootstrap repetitions differ, the smaller number will be used. Since pairwise differences are calculated for this test, the test function does not support multiple optimal cutpoints, because it is unclear how the differences should be calculated in that case. } \examples{ \dontrun{ library(cutpointr) library(dplyr) set.seed(734) cp_f <- cutpointr(suicide \%>\% filter(gender == "female"), dsi, suicide, boot_runs = 1000, boot_stratify = TRUE) set.seed(928) cp_m <- cutpointr(suicide \%>\% filter(gender == "male"), dsi, suicide, boot_runs = 1000, boot_stratify = TRUE) # No significant differences: boot_test(cp_f, cp_m, AUC, in_bag = TRUE) boot_test(cp_f, cp_m, sum_sens_spec, in_bag = FALSE) set.seed(135) cp <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 1000, boot_stratify = TRUE) # Roughly same result as above: boot_test(cp, variable = AUC, in_bag = TRUE) boot_test(cp, variable = sum_sens_spec, in_bag = FALSE) } } \seealso{ Other main cutpointr functions: \code{\link{add_metric}()}, \code{\link{boot_ci}()}, \code{\link{cutpointr}()}, \code{\link{multi_cutpointr}()}, \code{\link{predict.cutpointr}()}, \code{\link{roc}()} } \concept{main cutpointr functions} cutpointr/man/plot_metric.Rd0000644000176200001440000000320214726066534015676 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_metric.R \name{plot_metric} \alias{plot_metric} \title{Plot a metric over all possible cutoffs from a cutpointr object} \usage{ plot_metric(x, conf_lvl = 0.95, add_unsmoothed = TRUE) } \arguments{ \item{x}{A cutpointr object.} \item{conf_lvl}{The confidence level of the bootstrap confidence interval. Set to 0 to draw no bootstrap confidence interval.} \item{add_unsmoothed}{Add the line of unsmoothed metric values to the plot. Applicable for some smoothing methods, e.g. maximize_gam_metric.} } \description{ If \code{maximize_metric} is used as \code{method} function in cutpointr the computed metric values over all possible cutoffs can be plotted. Generally, this works for method functions that return a ROC-curve including the metric value for every cutpoint along with the optimal cutpoint. } \examples{ opt_cut <- cutpointr(suicide, dsi, suicide) plot_metric(opt_cut) } \seealso{ Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cut_boot}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_roc}()}, \code{\link{plot_sensitivity_specificity}()}, \code{\link{plot_x}()} Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cut_boot}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_roc}()}, \code{\link{plot_sensitivity_specificity}()}, \code{\link{plot_x}()} } \concept{cutpointr plotting functions} cutpointr/man/boot_ci.Rd0000644000176200001440000000362314726066534015002 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/boot_ci.R \name{boot_ci} \alias{boot_ci} \title{Calculate bootstrap confidence intervals from a cutpointr object} \usage{ boot_ci(x, variable, in_bag = TRUE, alpha = 0.05) } \arguments{ \item{x}{A cutpointr object with bootstrap results} \item{variable}{Variable to calculate CI for} \item{in_bag}{Whether the in-bag or out-of-bag results should be used for testing} \item{alpha}{Alpha level. Quantiles of the bootstrapped values are returned for (alpha / 2) and 1 - (alpha / 2).} } \value{ A data frame with the columns quantile and value } \description{ Given a \code{cutpointr} object that includes bootstrap results this function calculates a bootstrap confidence interval for a selected variable. Missing values are removed before calculating the quantiles. In the case of multiple optimal cutpoints all cutpoints / metric values are included in the calculation. Values of the selected variable are returned for the percentiles alpha / 2 and 1 - alpha / 2. The metrics in the bootstrap data frames of \code{cutpointr} are suffixed with \code{_b} and \code{_oob} to indicate in-bag and out-of-bag, respectively. For example, to calculate quantiles of the in-bag AUC \code{variable = AUC_b} should be set. } \examples{ \dontrun{ opt_cut <- cutpointr(suicide, dsi, suicide, gender, metric = youden, boot_runs = 1000) boot_ci(opt_cut, optimal_cutpoint, in_bag = FALSE, alpha = 0.05) boot_ci(opt_cut, acc, in_bag = FALSE, alpha = 0.05) boot_ci(opt_cut, cohens_kappa, in_bag = FALSE, alpha = 0.05) boot_ci(opt_cut, AUC, in_bag = TRUE, alpha = 0.05) } } \seealso{ Other main cutpointr functions: \code{\link{add_metric}()}, \code{\link{boot_test}()}, \code{\link{cutpointr}()}, \code{\link{multi_cutpointr}()}, \code{\link{predict.cutpointr}()}, \code{\link{roc}()} } \concept{main cutpointr functions} cutpointr/man/multi_cutpointr.Rd0000644000176200001440000000410514726066534016621 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cutpointr.R \name{multi_cutpointr} \alias{multi_cutpointr} \title{Calculate optimal cutpoints and further statistics for multiple predictors} \usage{ multi_cutpointr(data, x = NULL, class, subgroup = NULL, silent = FALSE, ...) } \arguments{ \item{data}{A data frame.} \item{x}{Character vector of predictor variables. If NULL all numeric columns.} \item{class}{The name of the outcome / independent variable.} \item{subgroup}{An additional covariate that identifies subgroups. Separate optimal cutpoints will be determined per group.} \item{silent}{Whether to suppress messages.} \item{...}{Further arguments to be passed to cutpointr, e.g., boot_runs} } \value{ A data frame. } \description{ Runs \code{cutpointr} over multiple predictor variables. Tidyeval via \code{!!} is supported for \code{class} and \code{subgroup}. If \code{x = NULL}, \code{cutpointr} will be run using all numeric columns in the data set as predictors except for the variable in \code{class} and, if given, \code{subgroup}. } \details{ The automatic determination of positive / negative classes and \code{direction} will be carried out separately for every predictor variable. That way, if \code{direction} and the classes are not specified, the reported AUC for every variable will be >= 0.5. AUC may be < 0.5 if subgroups are specified as \code{direction} is equal within every subgroup. } \examples{ library(cutpointr) multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide, pos_class = "yes") mcp <- multi_cutpointr(suicide, x = c("age", "dsi"), class = suicide, subgroup = gender, pos_class = "yes") mcp (scp <- summary(mcp)) \dontrun{ ## The result is a data frame tibble:::print.tbl(scp) } } \seealso{ Other main cutpointr functions: \code{\link{add_metric}()}, \code{\link{boot_ci}()}, \code{\link{boot_test}()}, \code{\link{cutpointr}()}, \code{\link{predict.cutpointr}()}, \code{\link{roc}()} } \concept{main cutpointr functions} cutpointr/man/plot_x.Rd0000644000176200001440000000223314726125624014661 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_x.R \name{plot_x} \alias{plot_x} \title{Plot the distribution of the independent variable per class from a cutpointr object} \usage{ plot_x(x, display_cutpoint = TRUE, ...) } \arguments{ \item{x}{A cutpointr object.} \item{display_cutpoint}{(logical) Whether or not to display the optimal cutpoint as a vertical line.} \item{...}{Additional arguments (unused).} } \description{ Given a \code{cutpointr} object this function plots the distribution(s) of the independent variable(s) and the respective cutpoints per class. } \examples{ opt_cut <- cutpointr(suicide, dsi, suicide) plot_x(opt_cut) ## With subgroup opt_cut_2groups <- cutpointr(suicide, dsi, suicide, gender) plot_x(opt_cut_2groups) } \seealso{ Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cut_boot}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_roc}()}, \code{\link{plot_sensitivity_specificity}()} } \concept{cutpointr plotting functions} cutpointr/man/predict.cutpointr.Rd0000644000176200001440000000245214726066534017043 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/predict.cutpointr.R \name{predict.cutpointr} \alias{predict.cutpointr} \title{Predict using a cutpointr object} \usage{ \method{predict}{cutpointr}(object, newdata, cutpoint_nr = 1, ...) } \arguments{ \item{object}{a cutpointr object.} \item{newdata}{a data.frame with a column that contains the predictor variable.} \item{cutpoint_nr}{if multiple optimal cutpoints were found this parameter defines which one should be used for predictions. Can be a vector if different cutpoint numbers are desired for different subgroups.} \item{...}{further arguments.} } \description{ Predictions are made on the \code{data.frame} in \code{newdata} using either the variable name or by applying the same transformation to the data as in \code{cutpointr}. The class of the output will be identical to the class of the predictor. } \examples{ oc <- cutpointr(suicide, dsi, suicide) ## Return in-sample predictions predict(oc, newdata = data.frame(dsi = oc$data[[1]]$dsi)) } \seealso{ Other main cutpointr functions: \code{\link{add_metric}()}, \code{\link{boot_ci}()}, \code{\link{boot_test}()}, \code{\link{cutpointr}()}, \code{\link{multi_cutpointr}()}, \code{\link{roc}()} } \concept{main cutpointr functions} cutpointr/man/oc_mean.Rd0000644000176200001440000000255314726125622014760 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/oc_mean.R \name{oc_mean} \alias{oc_mean} \title{Use the sample mean as cutpoint} \usage{ oc_mean(data, x, trim = 0, ...) } \arguments{ \item{data}{A data frame or tibble in which the columns that are given in x and class can be found.} \item{x}{(character) The variable name to be used for classification, e.g. predictions or test values.} \item{trim}{The fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.} \item{...}{To capture further arguments that are always passed to the method function by cutpointr. The cutpointr function passes data, x, class, metric_func, direction, pos_class and neg_class to the method function.} } \description{ The sample mean is calculated and returned as the optimal cutpoint. } \examples{ data(suicide) oc_mean(suicide, "dsi") cutpointr(suicide, dsi, suicide, method = oc_mean) } \seealso{ Other method functions: \code{\link{maximize_boot_metric}()}, \code{\link{maximize_gam_metric}()}, \code{\link{maximize_loess_metric}()}, \code{\link{maximize_metric}()}, \code{\link{oc_manual}()}, \code{\link{oc_median}()}, \code{\link{oc_youden_kernel}()}, \code{\link{oc_youden_normal}()} } \concept{method functions} cutpointr/man/Jaccard.Rd0000644000176200001440000000320014726125621014673 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{Jaccard} \alias{Jaccard} \title{Calculate the Jaccard Index} \usage{ Jaccard(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the Jaccard Index from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr Jaccard = (tp) / (tp + fp + fn) \cr } \examples{ Jaccard(10, 5, 20, 10) Jaccard(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/auc.Rd0000644000176200001440000000107314726066534014131 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{auc} \alias{auc} \alias{auc.roc_cutpointr} \alias{auc.cutpointr} \title{Calculate AUC from a roc_cutpointr or cutpointr object} \source{ Forked from the AUC package } \usage{ auc(x) \method{auc}{roc_cutpointr}(x) \method{auc}{cutpointr}(x) } \arguments{ \item{x}{Data frame resulting from the roc() or cutpointr() function.} } \value{ Numeric vector of AUC values } \description{ Calculate the area under the ROC curve using the trapezoidal rule. } cutpointr/man/roc01.Rd0000644000176200001440000000374414726125617014312 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{roc01} \alias{roc01} \title{Calculate the distance between points on the ROC curve and (0,1)} \usage{ roc01(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the distance on the ROC space between points on the ROC curve and the point of perfect discrimination from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. To be used with \code{method = minimize_metric}. \cr \cr sensitivity = tp / (tp + fn) \cr specificity = tn / (tn + fp) \cr roc01 = sqrt((1 - sensitivity)^2 + (1 - specificity)^2) \cr } \examples{ roc01(10, 5, 20, 10) roc01(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) oc <- cutpointr(suicide, dsi, suicide, method = minimize_metric, metric = roc01) plot_roc(oc) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/ppv.Rd0000644000176200001440000000320314726125617014161 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{ppv} \alias{ppv} \title{Calculate the positive predictive value} \usage{ ppv(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the positive predictive value (PPV) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr ppv = tp / (tp + fp) \cr } \examples{ ppv(10, 5, 20, 10) ppv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/cutpointr.Rd0000644000176200001440000003331514726126753015414 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cutpointr.R \name{cutpointr} \alias{cutpointr} \alias{cutpointr.default} \alias{cutpointr.numeric} \title{Determine and evaluate optimal cutpoints} \usage{ cutpointr(...) \method{cutpointr}{default}( data, x, class, subgroup = NULL, method = maximize_metric, metric = sum_sens_spec, pos_class = NULL, neg_class = NULL, direction = NULL, boot_runs = 0, boot_stratify = FALSE, use_midpoints = FALSE, break_ties = median, na.rm = FALSE, allowParallel = FALSE, silent = FALSE, tol_metric = 1e-06, ... ) \method{cutpointr}{numeric}( x, class, subgroup = NULL, method = maximize_metric, metric = sum_sens_spec, pos_class = NULL, neg_class = NULL, direction = NULL, boot_runs = 0, boot_stratify = FALSE, use_midpoints = FALSE, break_ties = median, na.rm = FALSE, allowParallel = FALSE, silent = FALSE, tol_metric = 1e-06, ... ) } \arguments{ \item{...}{Further optional arguments that will be passed to method. minimize_metric and maximize_metric pass ... to metric.} \item{data}{A data.frame with the data needed for x, class and optionally subgroup.} \item{x}{The variable name to be used for classification, e.g. predictions. The raw vector of values if the data argument is unused.} \item{class}{The variable name indicating class membership. If the data argument is unused, the vector of raw numeric values.} \item{subgroup}{An additional covariate that identifies subgroups or the raw data if data = NULL. Separate optimal cutpoints will be determined per group. Numeric, character and factor are allowed.} \item{method}{(function) A function for determining cutpoints. Can be user supplied or use some of the built in methods. See details.} \item{metric}{(function) The function for computing a metric when using maximize_metric or minimize_metric as method and and for the out-of-bag values during bootstrapping. A way of internally validating the performance. User defined functions can be supplied, see details.} \item{pos_class}{(optional) The value of class that indicates the positive class.} \item{neg_class}{(optional) The value of class that indicates the negative class.} \item{direction}{(character, optional) Use ">=" or "<=" to indicate whether x is supposed to be larger or smaller for the positive class.} \item{boot_runs}{(numerical) If positive, this number of bootstrap samples will be used to assess the variability and the out-of-sample performance.} \item{boot_stratify}{(logical) If the bootstrap is stratified, bootstrap samples are drawn separately in both classes and then combined, keeping the proportion of positives and negatives constant in every resample.} \item{use_midpoints}{(logical) If TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = ">=") or the next lowest observation (for direction = "<=") which avoids biasing the optimal cutpoint.} \item{break_ties}{If multiple cutpoints are found, they can be summarized using this function, e.g. mean or median. To return all cutpoints use c as the function.} \item{na.rm}{(logical) Set to TRUE (default FALSE) to keep only complete cases of x, class and subgroup (if specified). Missing values with na.rm = FALSE will raise an error.} \item{allowParallel}{(logical) If TRUE, the bootstrapping will be parallelized using foreach. A local cluster, for example, should be started manually beforehand.} \item{silent}{(logical) If TRUE suppresses all messages.} \item{tol_metric}{All cutpoints will be returned that lead to a metric value in the interval [m_max - tol_metric, m_max + tol_metric] where m_max is the maximum achievable metric value. This can be used to return multiple decent cutpoints and to avoid floating-point problems. Not supported by all \code{method} functions, see details.} } \value{ A cutpointr object which is also a data.frame and tbl_df. } \description{ Using predictions (or e.g. biological marker values) and binary class labels, this function will determine "optimal" cutpoints using various selectable methods. The methods for cutpoint determination can be evaluated using bootstrapping. An estimate of the cutpoint variability and the out-of-sample performance can then be returned with \code{summary} or \code{plot}. For an introduction to the package please see \code{vignette("cutpointr", package = "cutpointr")}. } \details{ If \code{direction} and/or \code{pos_class} and \code{neg_class} are not given, the function will assume that higher values indicate the positive class and use the class with a higher median as the positive class. This function uses tidyeval to support unquoted arguments. For programming with \code{cutpointr} the operator \code{!!} can be used to unquote an argument, see the examples. Different methods can be selected for determining the optimal cutpoint via the method argument. The package includes the following method functions: \itemize{ \item \code{maximize_metric}: Maximize the metric function \item \code{minimize_metric}: Minimize the metric function \item \code{maximize_loess_metric}: Maximize the metric function after LOESS smoothing \item \code{minimize_loess_metric}: Minimize the metric function after LOESS smoothing \item \code{maximize_boot_metric}: Maximize the metric function as a summary of the optimal cutpoints in bootstrapped samples \item \code{minimize_boot_metric}: Minimize the metric function as a summary of the optimal cutpoints in bootstrapped samples \item \code{oc_youden_kernel}: Maximize the Youden-Index after kernel smoothing the distributions of the two classes \item \code{oc_youden_normal}: Maximize the Youden-Index parametrically assuming normally distributed data in both classes \item \code{oc_manual}: Specify the cutpoint manually } User-defined functions can be supplied to method, too. As a reference, the code of all included method functions can be accessed by simply typing their name. To define a new method function, create a function that may take as input(s): \itemize{ \item \code{data}: A \code{data.frame} or \code{tbl_df} \item \code{x}: (character) The name of the predictor or independent variable \item \code{class}: (character) The name of the class or dependent variable \item \code{metric_func}: A function for calculating a metric, e.g. accuracy \item \code{pos_class}: The positive class \item \code{neg_class}: The negative class \item \code{direction}: ">=" if the positive class has higher x values, "<=" otherwise \item \code{tol_metric}: (numeric) In the built-in methods a tolerance around the optimal metric value \item \code{use_midpoints}: (logical) In the built-in methods whether to use midpoints instead of exact optimal cutpoints \item \code{...} Further arguments } The \code{...} argument can be used to avoid an error if not all of the above arguments are needed or in order to pass additional arguments to method. The function should return a \code{data.frame} or \code{tbl_df} with one row, the column "optimal_cutpoint", and an optional column with an arbitrary name with the metric value at the optimal cutpoint. Built-in metric functions include: \itemize{ \item \code{accuracy}: Fraction correctly classified \item \code{youden}: Youden- or J-Index = sensitivity + specificity - 1 \item \code{sum_sens_spec}: sensitivity + specificity \item \code{sum_ppv_npv}: The sum of positive predictive value (PPV) and negative predictive value (NPV) \item \code{prod_sens_spec}: sensitivity * specificity \item \code{prod_ppv_npv}: The product of positive predictive value (PPV) and negative predictive value (NPV) \item \code{cohens_kappa}: Cohen's Kappa \item \code{abs_d_sens_spec}: The absolute difference between sensitivity and specificity \item \code{roc01}: Distance to the point (0,1) on ROC space \item \code{abs_d_ppv_npv}: The absolute difference between positive predictive value (PPV) and negative predictive value (NPV) \item \code{p_chisquared}: The p-value of a chi-squared test on the confusion matrix of predictions and observations \item \code{odds_ratio}: The odds ratio calculated as (TP / FP) / (FN / TN) \item \code{risk_ratio}: The risk ratio (relative risk) calculated as (TP / (TP + FN)) / (FP / (FP + TN)) \item positive and negative likelihood ratio calculated as \code{plr} = true positive rate / false positive rate and \code{nlr} = false negative rate / true negative rate \item \code{misclassification_cost}: The sum of the misclassification cost of false positives and false negatives fp * cost_fp + fn * cost_fn. Additional arguments to cutpointr: \code{cost_fp}, \code{cost_fn} \item \code{total_utility}: The total utility of true / false positives / negatives calculated as utility_tp * TP + utility_tn * TN - cost_fp * FP - cost_fn * FN. Additional arguments to cutpointr: \code{utility_tp}, \code{utility_tn}, \code{cost_fp}, \code{cost_fn} \item \code{F1_score}: The F1-score (2 * TP) / (2 * TP + FP + FN) \item \code{sens_constrain}: Maximize sensitivity given a minimal value of specificity \item \code{spec_constrain}: Maximize specificity given a minimal value of sensitivity \item \code{metric_constrain}: Maximize a selected metric given a minimal value of another selected metric } Furthermore, the following functions are included which can be used as metric functions but are more useful for plotting purposes, for example in plot_cutpointr, or for defining new metric functions: \code{tp}, \code{fp}, \code{tn}, \code{fn}, \code{tpr}, \code{fpr}, \code{tnr}, \code{fnr}, \code{false_omission_rate}, \code{false_discovery_rate}, \code{ppv}, \code{npv}, \code{precision}, \code{recall}, \code{sensitivity}, and \code{specificity}. User defined metric functions can be created as well which can accept the following inputs as vectors: \itemize{ \item \code{tp}: Vector of true positives \item \code{fp}: Vector of false positives \item \code{tn}: Vector of true negatives \item \code{fn}: Vector of false negatives \item \code{...} If the metric function is used in conjunction with any of the maximize / minimize methods, further arguments can be passed } The function should return a numeric vector or a matrix or a \code{data.frame} with one column. If the column is named, the name will be included in the output and plots. Avoid using names that are identical to the column names that are by default returned by \pkg{cutpointr}. If \code{boot_runs} is positive, that number of bootstrap samples will be drawn and the optimal cutpoint using \code{method} will be determined. Additionally, as a way of internal validation, the function in \code{metric} will be used to score the out-of-bag predictions using the cutpoints determined by \code{method}. Various default metrics are always included in the bootstrap results. If multiple optimal cutpoints are found, the column optimal_cutpoint becomes a list that contains the vector(s) of the optimal cutpoints. If \code{use_midpoints = TRUE} the mean of the optimal cutpoint and the next highest or lowest possible cutpoint is returned, depending on \code{direction}. The \code{tol_metric} argument can be used to avoid floating-point problems that may lead to exclusion of cutpoints that achieve the optimally achievable metric value. Additionally, by selecting a large tolerance multiple cutpoints can be returned that lead to decent metric values in the vicinity of the optimal metric value. \code{tol_metric} is passed to metric and is only supported by the maximization and minimization functions, i.e. \code{maximize_metric}, \code{minimize_metric}, \code{maximize_loess_metric}, \code{minimize_loess_metric}. In \code{maximize_boot_metric} and \code{minimize_boot_metric} multiple optimal cutpoints will be passed to the \code{summary_func} of these two functions. } \examples{ library(cutpointr) ## Optimal cutpoint for dsi data(suicide) opt_cut <- cutpointr(suicide, dsi, suicide) opt_cut s_opt_cut <- summary(opt_cut) plot(opt_cut) \dontrun{ ## Predict class for new observations predict(opt_cut, newdata = data.frame(dsi = 0:5)) ## Supplying raw data, same result cutpointr(x = suicide$dsi, class = suicide$suicide) ## direction, class labels, method and metric can be defined manually ## Again, same result cutpointr(suicide, dsi, suicide, direction = ">=", pos_class = "yes", method = maximize_metric, metric = youden) ## Optimal cutpoint for dsi, as before, but for the separate subgroups opt_cut <- cutpointr(suicide, dsi, suicide, gender) opt_cut (s_opt_cut <- summary(opt_cut)) tibble:::print.tbl(s_opt_cut) ## Bootstrapping also works on individual subgroups set.seed(30) opt_cut <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 1000, boot_stratify = TRUE) opt_cut summary(opt_cut) plot(opt_cut) ## Parallelized bootstrapping library(doParallel) library(doRNG) cl <- makeCluster(2) # 2 cores registerDoParallel(cl) registerDoRNG(12) # Reproducible parallel loops using doRNG opt_cut <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 1000, allowParallel = TRUE) stopCluster(cl) opt_cut plot(opt_cut) ## Robust cutpoint method using kernel smoothing for optimizing Youden-Index opt_cut <- cutpointr(suicide, dsi, suicide, gender, method = oc_youden_kernel) opt_cut } } \seealso{ Other main cutpointr functions: \code{\link{add_metric}()}, \code{\link{boot_ci}()}, \code{\link{boot_test}()}, \code{\link{multi_cutpointr}()}, \code{\link{predict.cutpointr}()}, \code{\link{roc}()} } \concept{main cutpointr functions} cutpointr/man/print.multi_cutpointr.Rd0000644000176200001440000000115514726066534017756 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/print.multi_cutpointr.R \name{print.multi_cutpointr} \alias{print.multi_cutpointr} \title{Print multi_cutpointr objects} \source{ Kirill Müller and Hadley Wickham (2017). tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble } \usage{ \method{print}{multi_cutpointr}(x, n = Inf, ...) } \arguments{ \item{x}{a multi_cutpointr object.} \item{n}{number of rows to print.} \item{...}{further arguments.} } \description{ Prints the \code{multi_cutpointr} object with infinite width like a \code{tbl_df}. } cutpointr/man/plot_cut_boot.Rd0000644000176200001440000000201314726125623016223 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_cut_boot.R \name{plot_cut_boot} \alias{plot_cut_boot} \title{Plot the bootstrapped distribution of optimal cutpoints from a cutpointr object} \usage{ plot_cut_boot(x, ...) } \arguments{ \item{x}{A cutpointr object.} \item{...}{Additional arguments (unused).} } \description{ Given a cutpointr object this function plots the bootstrapped distribution of optimal cutpoints. \code{cutpointr} has to be run with \code{boot_runs}` > 0 to enable bootstrapping. } \examples{ set.seed(100) opt_cut <- cutpointr(suicide, dsi, suicide, boot_runs = 10) plot_cut_boot(opt_cut) } \seealso{ Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_roc}()}, \code{\link{plot_sensitivity_specificity}()}, \code{\link{plot_x}()} } \concept{cutpointr plotting functions} cutpointr/man/abs_d_ppv_npv.Rd0000644000176200001440000000344514726125620016176 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{abs_d_ppv_npv} \alias{abs_d_ppv_npv} \title{Calculate the absolute difference of positive and negative predictive value} \usage{ abs_d_ppv_npv(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the absolute difference of positive predictive value (PPV) and negative predictive value (NPV) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr ppv = tp / (tp + fp) \cr npv = tn / (tn + fn) \cr } \examples{ abs_d_ppv_npv(10, 5, 20, 10) abs_d_ppv_npv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/oc_youden_normal.Rd0000644000176200001440000000352214726125622016710 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/oc_youden_normal.R \name{oc_youden_normal} \alias{oc_youden_normal} \title{Determine an optimal cutpoint for the Youden-Index assuming normal distributions} \usage{ oc_youden_normal( data, x, class, pos_class = NULL, neg_class = NULL, direction, ... ) } \arguments{ \item{data}{A data frame or tibble in which the columns that are given in x and class can be found.} \item{x}{(character) The variable name to be used for classification, e.g. predictions or test values.} \item{class}{(character) The variable name indicating class membership.} \item{pos_class}{The value of class that indicates the positive class.} \item{neg_class}{The value of class that indicates the negative class.} \item{direction}{(character) Use ">=" or "<=" to select whether an x value >= or <= the cutoff predicts the positive class.} \item{...}{To capture further arguments that are always passed to the method function by cutpointr. The cutpointr function passes data, x, class, metric_func, direction, pos_class and neg_class to the method function.} } \description{ An optimal cutpoint maximizing the Youden- or J-Index (sensitivity + specificity - 1) is calculated parametrically assuming normal distributions per class. } \examples{ data(suicide) oc_youden_normal(suicide, "dsi", "suicide", pos_class = "yes", neg_class = "no", direction = ">=") cutpointr(suicide, dsi, suicide, method = oc_youden_normal) } \seealso{ Other method functions: \code{\link{maximize_boot_metric}()}, \code{\link{maximize_gam_metric}()}, \code{\link{maximize_loess_metric}()}, \code{\link{maximize_metric}()}, \code{\link{oc_manual}()}, \code{\link{oc_mean}()}, \code{\link{oc_median}()}, \code{\link{oc_youden_kernel}()} } \concept{method functions} cutpointr/man/p_chisquared.Rd0000644000176200001440000000320714726125621016022 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{p_chisquared} \alias{p_chisquared} \title{Calculate the p-value of a chi-squared test} \usage{ p_chisquared(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the p-value of a chi-squared test from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. } \examples{ p_chisquared(10, 5, 20, 10) p_chisquared(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/sum_sens_spec.Rd0000644000176200001440000000342014726125616016222 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{sum_sens_spec} \alias{sum_sens_spec} \title{Calculate the sum of sensitivity and specificity} \usage{ sum_sens_spec(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the sum of sensitivity and specificity from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr sensitivity = tp / (tp + fn) \cr specificity = tn / (tn + fp) \cr sum_sens_spec = sensitivity + specificity \cr } \examples{ sum_sens_spec(10, 5, 20, 10) sum_sens_spec(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/oc_manual.Rd0000644000176200001440000000222714726125621015312 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/oc_manual.R \name{oc_manual} \alias{oc_manual} \title{Set a manual cutpoint for use with cutpointr} \usage{ oc_manual(cutpoint, ...) } \arguments{ \item{cutpoint}{(numeric) The fixed cutpoint.} \item{...}{To capture further arguments that are always passed to the method function by cutpointr. The cutpointr function passes data, x, class, metric_func, direction, pos_class and neg_class to the method function.} } \description{ This function simply returns \code{cutpoint} as the optimal cutpoint. Mainly useful if bootstrap estimates of the out-of-bag performance of a given cutpoint are desired, e.g. taking a cutpoint value from the literature. } \examples{ cutpointr(suicide, dsi, suicide, method = oc_manual, cutpoint = 4) } \seealso{ Other method functions: \code{\link{maximize_boot_metric}()}, \code{\link{maximize_gam_metric}()}, \code{\link{maximize_loess_metric}()}, \code{\link{maximize_metric}()}, \code{\link{oc_mean}()}, \code{\link{oc_median}()}, \code{\link{oc_youden_kernel}()}, \code{\link{oc_youden_normal}()} } \concept{method functions} cutpointr/man/plot_sensitivity_specificity.Rd0000644000176200001440000000227014726125624021400 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_sensitivity_specificity.R \name{plot_sensitivity_specificity} \alias{plot_sensitivity_specificity} \title{Sensitivity and specificity plot from a cutpointr object} \usage{ plot_sensitivity_specificity(x, display_cutpoint = TRUE, ...) } \arguments{ \item{x}{A cutpointr object.} \item{display_cutpoint}{(logical) Whether or not to display the optimal cutpoint as a dot on the precision recall curve.} \item{...}{Additional arguments (unused).} } \description{ Given a \code{cutpointr} object this function plots the sensitivity and specificity curve(s) per subgroup, if the latter is given. } \examples{ library(cutpointr) ## Optimal cutpoint for dsi data(suicide) opt_cut <- cutpointr(suicide, dsi, suicide) plot_sensitivity_specificity(opt_cut) } \seealso{ Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cut_boot}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_roc}()}, \code{\link{plot_x}()} } \concept{cutpointr plotting functions} cutpointr/man/add_metric.Rd0000644000176200001440000000265614726066534015464 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/add_metric.R \name{add_metric} \alias{add_metric} \alias{add_metric.cutpointr} \alias{add_metric.multi_cutpointr} \alias{add_metric.roc_cutpointr} \title{Add metrics to a cutpointr or roc_cutpointr object} \usage{ add_metric(object, metric) \method{add_metric}{cutpointr}(object, metric) \method{add_metric}{multi_cutpointr}(object, metric) \method{add_metric}{roc_cutpointr}(object, metric) } \arguments{ \item{object}{A cutpointr or roc_cutpointr object.} \item{metric}{(list) A list of metric functions to be added.} } \value{ A cutpointr or roc_cutpointr object (a data.frame) with one or more added columns. } \description{ By default, the output of cutpointr includes the optimized metric and several other metrics. This function adds further metrics. Suitable metric functions are all metric functions that are included in the package or that comply with those standards. } \examples{ library(dplyr) library(cutpointr) cutpointr(suicide, dsi, suicide, gender) \%>\% add_metric(list(ppv, npv)) \%>\% select(optimal_cutpoint, subgroup, AUC, sum_sens_spec, ppv, npv) } \seealso{ Other main cutpointr functions: \code{\link{boot_ci}()}, \code{\link{boot_test}()}, \code{\link{cutpointr}()}, \code{\link{multi_cutpointr}()}, \code{\link{predict.cutpointr}()}, \code{\link{roc}()} } \concept{main cutpointr functions} cutpointr/man/youden.Rd0000644000176200001440000000332514726125616014663 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{youden} \alias{youden} \title{Calculate the Youden-Index} \usage{ youden(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the Youden-Index (J-Index) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr sensitivity = tp / (tp + fn) \cr specificity = tn / (tn + fp) \cr youden_index = sensitivity + specificity - 1 \cr } \examples{ youden(10, 5, 20, 10) youden(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()} } \concept{metric functions} cutpointr/man/plot_roc.Rd0000644000176200001440000000314614726125623015200 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_roc.R \name{plot_roc} \alias{plot_roc} \alias{plot_roc.cutpointr} \alias{plot_roc.roc_cutpointr} \title{Plot ROC curve from a cutpointr or roc_cutpointr object} \usage{ plot_roc(x, ...) \method{plot_roc}{cutpointr}(x, display_cutpoint = TRUE, type = "line", ...) \method{plot_roc}{roc_cutpointr}(x, type = "line", ...) } \arguments{ \item{x}{A cutpointr or roc_cutpointr object.} \item{...}{Additional arguments (unused).} \item{display_cutpoint}{(logical) Whether or not to display the optimal cutpoint as a dot on the ROC curve for cutpointr objects.} \item{type}{"line" for line plot (default) or "step" for step plot.} } \description{ Given a \code{cutpointr} object this function plots the ROC curve(s) per subgroup, if given. Also plots a ROC curve from the output of \code{roc()}. } \examples{ opt_cut <- cutpointr(suicide, dsi, suicide) plot_roc(opt_cut, display_cutpoint = FALSE) opt_cut_2groups <- cutpointr(suicide, dsi, suicide, gender) plot_roc(opt_cut_2groups, display_cutpoint = TRUE) roc_curve <- roc(suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") plot(roc_curve) auc(roc_curve) } \seealso{ Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cut_boot}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_sensitivity_specificity}()}, \code{\link{plot_x}()} } \concept{cutpointr plotting functions} cutpointr/man/F1_score.Rd0000644000176200001440000000320314726125621015010 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{F1_score} \alias{F1_score} \title{Calculate the F1-score} \usage{ F1_score(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the F1-score from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr F1_score = (2 * tp) / (2 * tp + fp + fn) \cr } \examples{ F1_score(10, 5, 20, 10) F1_score(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/total_utility.Rd0000644000176200001440000000423114726125621016257 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{total_utility} \alias{total_utility} \title{Calculate the total utility} \usage{ total_utility( tp, fp, tn, fn, utility_tp = 1, utility_tn = 1, cost_fp = 1, cost_fn = 1, ... ) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{utility_tp}{(numeric) the utility of a true positive} \item{utility_tn}{(numeric) the utility of a true negative} \item{cost_fp}{(numeric) the cost of a false positive} \item{cost_fn}{(numeric) the cost of a false negative} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the total utility from true positives, false positives, true negatives and false negatives. \cr \cr total_utility = utility_tp * tp + utility_tn * tn - cost_fp * fp - cost_fn * fn \cr \cr The inputs must be vectors of equal length. } \examples{ total_utility(10, 5, 20, 10, utility_tp = 3, utility_tn = 3, cost_fp = 1, cost_fn = 5) total_utility(c(10, 8), c(5, 7), c(20, 12), c(10, 18), utility_tp = 3, utility_tn = 3, cost_fp = 1, cost_fn = 5) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/maximize_loess_metric.Rd0000644000176200001440000001133514726125622017750 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/optimize_metric.R \name{maximize_loess_metric} \alias{maximize_loess_metric} \alias{minimize_loess_metric} \title{Optimize a metric function in binary classification after LOESS smoothing} \source{ Xiao-Feng Wang (2010). fANCOVA: Nonparametric Analysis of Covariance. https://CRAN.R-project.org/package=fANCOVA Leeflang, M. M., Moons, K. G., Reitsma, J. B., & Zwinderman, A. H. (2008). Bias in sensitivity and specificity caused by data-driven selection of optimal cutoff values: mechanisms, magnitude, and solutions. Clinical Chemistry, (4), 729–738. } \usage{ maximize_loess_metric( data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, criterion = "aicc", degree = 1, family = "symmetric", user.span = NULL, tol_metric, use_midpoints, ... ) minimize_loess_metric( data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, criterion = "aicc", degree = 1, family = "symmetric", user.span = NULL, tol_metric, use_midpoints, ... ) } \arguments{ \item{data}{A data frame or tibble in which the columns that are given in x and class can be found.} \item{x}{(character) The variable name to be used for classification, e.g. predictions or test values.} \item{class}{(character) The variable name indicating class membership.} \item{metric_func}{(function) A function that computes a metric to be maximized. See description.} \item{pos_class}{The value of class that indicates the positive class.} \item{neg_class}{The value of class that indicates the negative class.} \item{direction}{(character) Use ">=" or "<=" to select whether an x value >= or <= the cutoff predicts the positive class.} \item{criterion}{the criterion for automatic smoothing parameter selection: "aicc" denotes bias-corrected AIC criterion, "gcv" denotes generalized cross-validation.} \item{degree}{the degree of the local polynomials to be used. It can be 0, 1 or 2.} \item{family}{if "gaussian" fitting is by least-squares, and if "symmetric" a re-descending M estimator is used with Tukey's biweight function.} \item{user.span}{The user-defined parameter which controls the degree of smoothing} \item{tol_metric}{All cutpoints will be returned that lead to a metric value in the interval [m_max - tol_metric, m_max + tol_metric] where m_max is the maximum achievable metric value. This can be used to return multiple decent cutpoints and to avoid floating-point problems.} \item{use_midpoints}{(logical) If TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = ">") or the next lowest observation (for direction = "<") which avoids biasing the optimal cutpoint.} \item{...}{Further arguments that will be passed to metric_func or the loess smoother.} } \value{ A tibble with the columns \code{optimal_cutpoint}, the corresponding metric value and \code{roc_curve}, a nested tibble that includes all possible cutoffs and the corresponding numbers of true and false positives / negatives and all corresponding metric values. } \description{ Given a function for computing a metric in \code{metric_func}, these functions smooth the function of metric value per cutpoint using LOESS, then maximize or minimize the metric by selecting an optimal cutpoint. For further details on the LOESS smoothing see \code{?fANCOVA::loess.as}. The \code{metric} function should accept the following inputs: \itemize{ \item \code{tp}: vector of number of true positives \item \code{fp}: vector of number of false positives \item \code{tn}: vector of number of true negatives \item \code{fn}: vector of number of false negatives } } \details{ The above inputs are arrived at by using all unique values in \code{x}, Inf, and -Inf as possible cutpoints for classifying the variable in class. } \examples{ oc <- cutpointr(suicide, dsi, suicide, gender, method = maximize_loess_metric, criterion = "aicc", family = "symmetric", degree = 2, user.span = 0.7, metric = accuracy) plot_metric(oc) oc <- cutpointr(suicide, dsi, suicide, gender, method = minimize_loess_metric, criterion = "aicc", family = "symmetric", degree = 2, user.span = 0.7, metric = misclassification_cost, cost_fp = 1, cost_fn = 10) plot_metric(oc) } \seealso{ Other method functions: \code{\link{maximize_boot_metric}()}, \code{\link{maximize_gam_metric}()}, \code{\link{maximize_metric}()}, \code{\link{oc_manual}()}, \code{\link{oc_mean}()}, \code{\link{oc_median}()}, \code{\link{oc_youden_kernel}()}, \code{\link{oc_youden_normal}()} } \concept{method functions} cutpointr/man/figures/0000755000176200001440000000000014726066534014535 5ustar liggesuserscutpointr/man/figures/README-unnamed-chunk-37-1.png0000644000176200001440000001132014726066534021315 0ustar liggesusersPNG  IHDR TcPLTE:f:::f:fHqHq::::f::::f:::ff:f:f::HHHHqHHHqHMMMMMnMMMnM^adff:fff:f:fffffffnMMnMnnMnnnnnnqqHqqqHqqqqMMMnMnMn:::ff::fې۶HHHqqݺnMnnMȫff::ې۶۶qqHȎMې:ېf۶f۶۶ݗHnvmfqȎې۶ݗ[C pHYsodIDATx흍6ǝ[/Keve({vP `WȀuvۺ͚6p݊l9eŎ%KOH'!JbB" D,Y @d0Nz6t#Ov;CN9{tuفQ^2$ɞd) #s<{8S2kq$YD,@%g4zGѺ*!B" D,Y @d!B""fu$+MZk7ml#lODȳrMdfQ!&E`!̌L{Ig [뱉X$} .xfOc%T)Wx'IP8]C,mJO(q+%9&$]^aD/P90uLw#ؔlNvԥ`pǀ0*N*'Km\~ =4k<3ж$ufX,{SDT"oXZvX?+ft_(=dUY:$oT(mm;M3 { zUOZ-*^&l^W|-wݍdQ.YViC~*UKg0mDV?"읨%;S8'ʒR`0~G ^iX. o\t!h{* ,MG.W s+)#AVZ.j:a[aS.s:<::a~āh¶wN+;aBqpѳj4:aJU6iD.v>a(Xu6ŶV u-.(ݝGȃvX-P+O#7@^7(/g,jjǗ>&~Xdh++LZ؞Hp|ba(%> ơnR`iy7se> !n> <&ɢU@6}h5@n 52 ҊNA*2ܹuΝ"-afU?G^yT6% k4DkFX!afVkFX!afVkFX!aUgd3pg\VyKTDfVkFX!afVkFX!afVkFX!afVkFXëxsCϾ#\@8Ǘ!Ȕ<,pq}U5@-@*j ~G T\u!~:jBX 菟{3}=^ wylZK$I:/Y5nCo${jCXa 07䖠p[ɴb9L& 8M:WGW IL厂``j$mohvХѴN]3ĜG3# wo-'9};l7w@M8p)0dwHv{a}@%*g.<0}U ˆ[dԙ\TB^3L ӝt;3 Le:a@hKg#Lp- C"sB^3k1;[k)I"I SLafIa 0%Bͺ,@rBUupVƕV*"F^3 v+N-W- TgP);ܪCń@Fm!Wu--IFYyWθ|/sB yi. 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The inputs must be vectors of equal length. \cr \cr npv = tn / (tn + fn) \cr } \examples{ npv(10, 5, 20, 10) npv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/metric_constrain.Rd0000644000176200001440000000765014726125616016730 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{metric_constrain} \alias{metric_constrain} \alias{sens_constrain} \alias{spec_constrain} \alias{acc_constrain} \title{Metrics that are constrained by another metric} \usage{ metric_constrain( tp, fp, tn, fn, main_metric = sensitivity, constrain_metric = specificity, min_constrain = 0.5, suffix = "_constrain", ... ) sens_constrain( tp, fp, tn, fn, constrain_metric = specificity, min_constrain = 0.5, ... ) spec_constrain( tp, fp, tn, fn, constrain_metric = sensitivity, min_constrain = 0.5, ... ) acc_constrain( tp, fp, tn, fn, constrain_metric = sensitivity, min_constrain = 0.5, ... ) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{main_metric}{Metric to be optimized.} \item{constrain_metric}{Metric for constraint.} \item{min_constrain}{Minimum desired value of constrain_metric.} \item{suffix}{Character string to be added to the name of main_metric.} \item{...}{for capturing additional arguments passed by method.} } \description{ For example, calculate sensitivity where a lower bound (minimal desired value) for specificty can be defined. All returned metric values for cutpoints that lead to values of the constraining metric below the specified minimum will be zero. The inputs must be vectors of equal length. } \examples{ ## Maximum sensitivity when Positive Predictive Value (PPV) is at least 75\% library(dplyr) library(purrr) library(cutpointr) cp <- cutpointr(data = suicide, x = dsi, class = suicide, method = maximize_metric, metric = sens_constrain, constrain_metric = ppv, min_constrain = 0.75) ## All metric values (m) where PPV < 0.75 are zero plot_metric(cp) cp$roc_curve ## We can confirm that PPV is indeed >= 0.75 cp \%>\% add_metric(list(ppv)) ## We can also do so for the complete ROC curve(s) cp \%>\% pull(roc_curve) \%>\% map(~ add_metric(., list(sensitivity, ppv))) ## Use the metric_constrain function for a combination of any two metrics ## Estimate optimal cutpoint for precision given a recall of at least 70\% cp <- cutpointr(data = suicide, x = dsi, class = suicide, subgroup = gender, method = maximize_metric, metric = metric_constrain, main_metric = precision, suffix = "_constrained", constrain_metric = recall, min_constrain = 0.70) ## All metric values (m) where recall < 0.7 are zero plot_metric(cp) ## We can confirm that recall is indeed >= 0.70 and that precision_constrain ## is identical to precision for the estimated cutpoint cp \%>\% add_metric(list(recall, precision)) ## We can also do so for the complete ROC curve(s) cp \%>\% pull(roc_curve) \%>\% map(~ add_metric(., list(recall, precision))) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/specificity.Rd0000644000176200001440000000302314726125616015666 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{specificity} \alias{specificity} \title{Calculate specificity} \usage{ specificity(fp, tn, ...) } \arguments{ \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate specificity from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr specificity = tn / (tn + fp) \cr } \examples{ specificity(10, 5, 20, 10) specificity(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/misclassification_cost.Rd0000644000176200001440000000367514726125621020120 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{misclassification_cost} \alias{misclassification_cost} \title{Calculate the misclassification cost} \usage{ misclassification_cost(tp, fp, tn, fn, cost_fp = 1, cost_fn = 1, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{cost_fp}{(numeric) the cost of a false positive} \item{cost_fn}{(numeric) the cost of a false negative} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the misclassification cost from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr misclassification_cost = cost_fp * fp + cost_fn * fn \cr } \examples{ misclassification_cost(10, 5, 20, 10, cost_fp = 1, cost_fn = 5) misclassification_cost(c(10, 8), c(5, 7), c(20, 12), c(10, 18), cost_fp = 1, cost_fn = 5) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/plot_metric_boot.Rd0000644000176200001440000000225214726066534016725 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_metric_boot.R \name{plot_metric_boot} \alias{plot_metric_boot} \title{Plot the bootstrapped metric distribution from a cutpointr object} \usage{ plot_metric_boot(x, ...) } \arguments{ \item{x}{A cutpointr object.} \item{...}{Additional arguments (unused)} } \description{ Given a \code{cutpointr} object this function plots the bootstrapped metric distribution, i.e. the distribution of out-of-bag metric values. The metric depends on the function that was supplied to \code{metric} in the call to \code{cutpointr}. The \code{cutpointr} function has to be run with \code{boot_runs}` > 0 to enable bootstrapping. } \examples{ set.seed(300) opt_cut <- cutpointr(suicide, dsi, suicide, boot_runs = 10) plot_metric_boot(opt_cut) } \seealso{ Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cut_boot}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_roc}()}, \code{\link{plot_sensitivity_specificity}()}, \code{\link{plot_x}()} } \concept{cutpointr plotting functions} cutpointr/man/odds_ratio.Rd0000644000176200001440000000322614726125621015503 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{odds_ratio} \alias{odds_ratio} \title{Calculate the odds ratio} \usage{ odds_ratio(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the (diagnostic) odds ratio from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr odds_ratio = (tp / fp) / (fn / tn) \cr } \examples{ odds_ratio(10, 5, 20, 10) odds_ratio(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/prostate_nodal.Rd0000644000176200001440000000173114726066534016400 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{prostate_nodal} \alias{prostate_nodal} \title{Nodal involvement and acid phosphatase levels in 53 prostate cancer patients} \format{ A data frame with 53 rows and 2 variables: \describe{ \item{acid_phosphatase}{(numeric) Blood serum level of acid phosphatase} \item{nodal_involvement}{(logical) Whether neighboring lymph nodes were involved} } } \source{ Le CT (2006). A solution for the most basic optimization problem associated with an ROC curve. Statistical methods in medical research 15: 571–584 } \usage{ prostate_nodal } \description{ Prostatic acid phosphatase (PAP) emerged as the first clinically useful tumor marker in the 1940s and 1950s. This data set contains the serum levels of acid phosphatase of 53 patients that were confirmed to have prostate cancer and whether the neighboring lymph nodes were involved. } \keyword{datasets} cutpointr/man/precision.Rd0000644000176200001440000000323414726125617015353 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{precision} \alias{precision} \title{Calculate precision} \usage{ precision(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate precision (equal to the positive predictive value) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr precision = tp / (tp + fp) \cr } \examples{ precision(10, 5, 20, 10) precision(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/false_omission_rate.Rd0000644000176200001440000000355714726125620017407 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{false_omission_rate} \alias{false_omission_rate} \alias{false_discovery_rate} \title{Calculate the false omission and false discovery rate} \usage{ false_omission_rate(tp, fp, tn, fn, ...) false_discovery_rate(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the false omission rate or false discovery rate from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr false_omission_rate = fn / (tn + fn) = 1 - npv false_discovery_rate = fp / (tp + fp) = 1 - ppv } \examples{ false_omission_rate(10, 5, 20, 10) false_omission_rate(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/cutpointr_.Rd0000644000176200001440000000750514726066534015555 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cutpointr.R \name{cutpointr_} \alias{cutpointr_} \title{The standard evaluation version of cutpointr (deprecated)} \usage{ cutpointr_( data, x, class, subgroup = NULL, method = maximize_metric, metric = sum_sens_spec, pos_class = NULL, neg_class = NULL, direction = NULL, boot_runs = 0, boot_stratify = FALSE, use_midpoints = FALSE, break_ties = median, na.rm = FALSE, allowParallel = FALSE, silent = FALSE, tol_metric = 1e-06, ... ) } \arguments{ \item{data}{A data.frame with the data needed for x, class and optionally subgroup.} \item{x}{(character) The variable name to be used for classification, e.g. predictions or test values.} \item{class}{(character) The variable name indicating class membership.} \item{subgroup}{(character) The variable name of an additional covariate that identifies subgroups. Separate optimal cutpoints will be determined per group.} \item{method}{(function) A function for determining cutpoints. Can be user supplied or use some of the built in methods. See details.} \item{metric}{(function) The function for computing a metric when using maximize_metric or minimize_metric as method and and for the out-of-bag values during bootstrapping. A way of internally validating the performance. User defined functions can be supplied, see details.} \item{pos_class}{(optional) The value of class that indicates the positive class.} \item{neg_class}{(optional) The value of class that indicates the negative class.} \item{direction}{(character, optional) Use ">=" or "<=" to indicate whether x is supposed to be larger or smaller for the positive class.} \item{boot_runs}{(numerical) If positive, this number of bootstrap samples will be used to assess the variability and the out-of-sample performance.} \item{boot_stratify}{(logical) If the bootstrap is stratified, bootstrap samples are drawn separately in both classes and then combined, keeping the proportion of positives and negatives constant in every resample.} \item{use_midpoints}{(logical) If TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = ">=") or the next lowest observation (for direction = "<=") which avoids biasing the optimal cutpoint.} \item{break_ties}{If multiple cutpoints are found, they can be summarized using this function, e.g. mean or median. To return all cutpoints use c as the function.} \item{na.rm}{(logical) Set to TRUE (default FALSE) to keep only complete cases of x, class and subgroup (if specified). Missing values with na.rm = FALSE will raise an error.} \item{allowParallel}{(logical) If TRUE, the bootstrapping will be parallelized using foreach. A local cluster, for example, should be started manually beforehand.} \item{silent}{(logical) If TRUE suppresses all messages.} \item{tol_metric}{All cutpoints will be returned that lead to a metric value in the interval [m_max - tol_metric, m_max + tol_metric] where m_max is the maximum achievable metric value. This can be used to return multiple decent cutpoints and to avoid floating-point problems. Not supported by all \code{method} functions, see details.} \item{...}{Further optional arguments that will be passed to method. minimize_metric and maximize_metric pass ... to metric.} } \description{ This function is equivalent to \code{cutpointr} but takes only quoted arguments for \code{x}, \code{class} and \code{subgroup}. This was useful before \code{cutpointr} supported tidyeval. } \examples{ library(cutpointr) ## Optimal cutpoint for dsi data(suicide) opt_cut <- cutpointr_(suicide, "dsi", "suicide") opt_cut summary(opt_cut) plot(opt_cut) predict(opt_cut, newdata = data.frame(dsi = 0:5)) } cutpointr/man/suicide.Rd0000644000176200001440000000253114726066534015006 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{suicide} \alias{suicide} \title{Suicide attempts and DSI sum scores of 532 subjects} \format{ A data frame with 532 rows and 4 variables: \describe{ \item{age}{(numeric) Age of participants in years} \item{gender}{(factor) Gender} \item{dsi}{(numeric) Sum-score (0 = low suicidality, 12 = high suicidality)} \item{suicide}{(factor) Past suicide attempt (no = no attempt, yes = at least one attempt)} } } \source{ von Glischinski, M., Teisman, T., Prinz, S., Gebauer, J., and Hirschfeld, G. (2017). Depressive Symptom Inventory- Suicidality Subscale: Optimal cut points for clinical and non-clinical samples. Clinical Psychology & Psychotherapy } \usage{ suicide } \description{ Various personality and clinical psychological characteristics were assessed as part of an online-study preventing suicide. To identify persons at risk for attempting suicide, various demographic and clinical characteristics were assessed. Depressive Symptom Inventory - Suicidality Subscale (DSA-SS) sum scores and past suicide attempts from 532 subjects are included as a demonstration set to calculate optimal cutpoints. Two additional demographic variables (age, gender) are also included to test for group differences. } \keyword{datasets} cutpointr/man/accuracy.Rd0000644000176200001440000000317114726125616015151 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{accuracy} \alias{accuracy} \title{Calculate accuracy} \usage{ accuracy(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate accuracy from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr accuracy = (tp + tn) / (tp + fp + tn + fn) } \examples{ accuracy(10, 5, 20, 10) accuracy(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/plot.roc_cutpointr.Rd0000644000176200001440000000244714726125623017231 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_roc.R \name{plot.roc_cutpointr} \alias{plot.roc_cutpointr} \title{Plot ROC curve from a cutpointr or roc_cutpointr object} \usage{ \method{plot}{roc_cutpointr}(x, type = "line", ...) } \arguments{ \item{x}{A cutpointr or roc_cutpointr object.} \item{type}{"line" for line plot (default) or "step" for step plot.} \item{...}{Additional arguments (unused).} } \description{ Given a \code{cutpointr} object this function plots the ROC curve(s) per subgroup, if given. Also plots a ROC curve from the output of \code{roc()}. } \examples{ opt_cut <- cutpointr(suicide, dsi, suicide) plot_roc(opt_cut, display_cutpoint = FALSE) opt_cut_2groups <- cutpointr(suicide, dsi, suicide, gender) plot_roc(opt_cut_2groups, display_cutpoint = TRUE) roc_curve <- roc(suicide, x = dsi, class = suicide, pos_class = "yes", neg_class = "no", direction = ">=") plot(roc_curve) auc(roc_curve) } \seealso{ Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cut_boot}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_precision_recall}()}, \code{\link{plot_sensitivity_specificity}()}, \code{\link{plot_x}()} } cutpointr/man/abs_d_sens_spec.Rd0000644000176200001440000000347514726125617016501 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{abs_d_sens_spec} \alias{abs_d_sens_spec} \title{Calculate the absolute difference of sensitivity and specificity} \usage{ abs_d_sens_spec(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the absolute difference of sensitivity and specificity from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr sensitivity = tp / (tp + fn) \cr specificity = tn / (tn + fp) \cr abs_d_sens_spec = |sensitivity - specificity| \cr } \examples{ abs_d_sens_spec(10, 5, 20, 10) abs_d_sens_spec(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/tp.Rd0000644000176200001440000000340514726066534014005 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{tp} \alias{tp} \alias{tn} \alias{fp} \alias{fn} \title{Extract number true / false positives / negatives} \usage{ tp(tp, ...) tn(tn, ...) fp(fp, ...) fn(fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{...}{for capturing additional arguments passed by method.} \item{tn}{(numeric) number of true negatives.} \item{fp}{(numeric) number of false positives.} \item{fn}{(numeric) number of false negatives.} } \description{ Extract the number of true positives (tp), false positives (fp), true negatives (tn), or false negatives (fn). The inputs must be vectors of equal length. Mainly useful for \code{plot_cutpointr}. } \examples{ tp(10, 5, 20, 10) tp(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) fp(10, 5, 20, 10) tn(10, 5, 20, 10) fn(10, 5, 20, 10) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/plot.multi_cutpointr.Rd0000644000176200001440000000066514726066534017605 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.multi_cutpointr.R \name{plot.multi_cutpointr} \alias{plot.multi_cutpointr} \title{Plotting multi_cutpointr objects is currently not supported} \usage{ \method{plot}{multi_cutpointr}(x, ...) } \arguments{ \item{x}{A multi_cutpointr object.} \item{...}{Further arguments.} } \description{ You can try plotting the data manually instead. } cutpointr/man/maximize_gam_metric.Rd0000644000176200001440000001000714726125623017363 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/optimize_metric.R \name{maximize_gam_metric} \alias{maximize_gam_metric} \alias{minimize_gam_metric} \title{Optimize a metric function in binary classification after smoothing via generalized additive models} \usage{ maximize_gam_metric( data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, formula = m ~ s(x.sorted), optimizer = c("outer", "newton"), tol_metric, use_midpoints, ... ) minimize_gam_metric( data, x, class, metric_func = youden, pos_class = NULL, neg_class = NULL, direction, formula = m ~ s(x.sorted), optimizer = c("outer", "newton"), tol_metric, use_midpoints, ... ) } \arguments{ \item{data}{A data frame or tibble in which the columns that are given in x and class can be found.} \item{x}{(character) The variable name to be used for classification, e.g. predictions or test values.} \item{class}{(character) The variable name indicating class membership.} \item{metric_func}{(function) A function that computes a metric to be maximized. See description.} \item{pos_class}{The value of class that indicates the positive class.} \item{neg_class}{The value of class that indicates the negative class.} \item{direction}{(character) Use ">=" or "<=" to select whether an x value >= or <= the cutoff predicts the positive class.} \item{formula}{A GAM formula. See \code{help("gam", package = "mgcv")} for details.} \item{optimizer}{An array specifying the numerical optimization method to use to optimize the smoothing parameter estimation criterion (given by method). See \code{help("gam", package = "mgcv")} for details.} \item{tol_metric}{All cutpoints will be returned that lead to a metric value in the interval [m_max - tol_metric, m_max + tol_metric] where m_max is the maximum achievable metric value. This can be used to return multiple decent cutpoints and to avoid floating-point problems.} \item{use_midpoints}{(logical) If TRUE (default FALSE) the returned optimal cutpoint will be the mean of the optimal cutpoint and the next highest observation (for direction = ">") or the next lowest observation (for direction = "<") which avoids biasing the optimal cutpoint.} \item{...}{Further arguments that will be passed to metric_func or the GAM smoother.} } \value{ A tibble with the columns \code{optimal_cutpoint}, the corresponding metric value and \code{roc_curve}, a nested tibble that includes all possible cutoffs and the corresponding numbers of true and false positives / negatives and all corresponding metric values. } \description{ Given a function for computing a metric in \code{metric_func}, these functions smooth the function of metric value per cutpoint using generalized additive models (as implemented in \pkg{mgcv}), then maximize or minimize the metric by selecting an optimal cutpoint. For further details on the GAM smoothing see \code{?mgcv::gam}. The \code{metric} function should accept the following inputs: \itemize{ \item \code{tp}: vector of number of true positives \item \code{fp}: vector of number of false positives \item \code{tn}: vector of number of true negatives \item \code{fn}: vector of number of false negatives } } \details{ The above inputs are arrived at by using all unique values in \code{x}, Inf, and -Inf as possible cutpoints for classifying the variable in class. } \examples{ oc <- cutpointr(suicide, dsi, suicide, gender, method = maximize_gam_metric, metric = accuracy) plot_metric(oc) oc <- cutpointr(suicide, dsi, suicide, gender, method = minimize_gam_metric, metric = abs_d_sens_spec) plot_metric(oc) } \seealso{ Other method functions: \code{\link{maximize_boot_metric}()}, \code{\link{maximize_loess_metric}()}, \code{\link{maximize_metric}()}, \code{\link{oc_manual}()}, \code{\link{oc_mean}()}, \code{\link{oc_median}()}, \code{\link{oc_youden_kernel}()}, \code{\link{oc_youden_normal}()} } \concept{method functions} cutpointr/man/plot_precision_recall.Rd0000644000176200001440000000217014726125623017726 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_precision_recall.R \name{plot_precision_recall} \alias{plot_precision_recall} \title{Precision recall plot from a cutpointr object} \usage{ plot_precision_recall(x, display_cutpoint = TRUE, ...) } \arguments{ \item{x}{A cutpointr object.} \item{display_cutpoint}{(logical) Whether or not to display the optimal cutpoint as a dot on the precision recall curve.} \item{...}{Additional arguments (unused).} } \description{ Given a \code{cutpointr} object this function plots the precision recall curve(s) per subgroup, if given. } \examples{ library(cutpointr) ## Optimal cutpoint for dsi data(suicide) opt_cut <- cutpointr(suicide, dsi, suicide) plot_precision_recall(opt_cut) } \seealso{ Other cutpointr plotting functions: \code{\link{plot.cutpointr}()}, \code{\link{plot_cut_boot}()}, \code{\link{plot_cutpointr}()}, \code{\link{plot_metric}()}, \code{\link{plot_metric_boot}()}, \code{\link{plot_roc}()}, \code{\link{plot_sensitivity_specificity}()}, \code{\link{plot_x}()} } \concept{cutpointr plotting functions} cutpointr/man/tpr.Rd0000644000176200001440000000373114726066534014171 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{tpr} \alias{tpr} \alias{fpr} \alias{tnr} \alias{fnr} \title{Calculate true / false positive / negative rate} \usage{ tpr(tp, fn, ...) fpr(fp, tn, ...) tnr(fp, tn, ...) fnr(tp, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} } \description{ Calculate the true positive rate (tpr, equal to sensitivity and recall), the false positive rate (fpr, equal to fall-out), the true negative rate (tnr, equal to specificity), or the false negative rate (fnr) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr tpr = tp / (tp + fn) \cr fpr = fp / (fp + tn) \cr tnr = tn / (tn + fp) \cr fnr = fn / (fn + tp) \cr } \examples{ tpr(10, 5, 20, 10) tpr(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/prod_ppv_npv.Rd0000644000176200001440000000344714726125620016074 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{prod_ppv_npv} \alias{prod_ppv_npv} \title{Calculate the product of positive and negative predictive value} \usage{ prod_ppv_npv(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate the product of positive predictive value (PPV) and negative predictive value (NPV) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr ppv = tp / (tp + fp) \cr npv = tn / (tn + fn) \cr prod_ppv_npv = ppv * npv \cr } \examples{ prod_ppv_npv(10, 5, 20, 10) prod_ppv_npv(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_sens_spec}()}, \code{\link{recall}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/user_span_cutpointr.Rd0000644000176200001440000000150514726066534017467 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/optimize_metric.R \name{user_span_cutpointr} \alias{user_span_cutpointr} \title{Calculate bandwidth for LOESS smoothing of metric functions by rule of thumb} \usage{ user_span_cutpointr(data, x) } \arguments{ \item{data}{A data frame} \item{x}{The predictor variable} } \description{ This function implements a rule of thumb for selecting the bandwidth when smoothing a function of metric values per cutpoint value, particularly in \code{maximize_loess_metric} and \code{minimize_loess_metric}. } \details{ The function used for calculating the bandwidth is 0.1 * xsd / sqrt(xn), where xsd is the standard deviation of the unique values of the predictor variable (i.e. all cutpoints) and xn is the number of unique predictor values. } cutpointr/man/recall.Rd0000644000176200001440000000316514726125617014625 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/metrics.R \name{recall} \alias{recall} \title{Calculate recall} \usage{ recall(tp, fp, tn, fn, ...) } \arguments{ \item{tp}{(numeric) number of true positives.} \item{fp}{(numeric) number of false positives.} \item{tn}{(numeric) number of true negatives.} \item{fn}{(numeric) number of false negatives.} \item{...}{for capturing additional arguments passed by method.} } \description{ Calculate recall (equal to sensitivity) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. \cr \cr recall = tp / (tp + fn) \cr } \examples{ recall(10, 5, 20, 10) recall(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) } \seealso{ Other metric functions: \code{\link{F1_score}()}, \code{\link{Jaccard}()}, \code{\link{abs_d_ppv_npv}()}, \code{\link{abs_d_sens_spec}()}, \code{\link{accuracy}()}, \code{\link{cohens_kappa}()}, \code{\link{cutpoint}()}, \code{\link{false_omission_rate}()}, \code{\link{metric_constrain}()}, \code{\link{misclassification_cost}()}, \code{\link{npv}()}, \code{\link{odds_ratio}()}, \code{\link{p_chisquared}()}, \code{\link{plr}()}, \code{\link{ppv}()}, \code{\link{precision}()}, \code{\link{prod_ppv_npv}()}, \code{\link{prod_sens_spec}()}, \code{\link{risk_ratio}()}, \code{\link{roc01}()}, \code{\link{sensitivity}()}, \code{\link{specificity}()}, \code{\link{sum_ppv_npv}()}, \code{\link{sum_sens_spec}()}, \code{\link{total_utility}()}, \code{\link{tp}()}, \code{\link{tpr}()}, \code{\link{youden}()} } \concept{metric functions} cutpointr/man/oc_median.Rd0000644000176200001440000000226214726125622015272 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/oc_median.R \name{oc_median} \alias{oc_median} \title{Use the sample median as cutpoint} \usage{ oc_median(data, x, ...) } \arguments{ \item{data}{A data frame or tibble in which the columns that are given in x and class can be found.} \item{x}{(character) The variable name to be used for classification, e.g. predictions or test values.} \item{...}{To capture further arguments that are always passed to the method function by cutpointr. The cutpointr function passes data, x, class, metric_func, direction, pos_class and neg_class to the method function.} } \description{ The sample median is calculated and returned as the optimal cutpoint. } \examples{ data(suicide) oc_median(suicide, "dsi") cutpointr(suicide, dsi, suicide, method = oc_median) } \seealso{ Other method functions: \code{\link{maximize_boot_metric}()}, \code{\link{maximize_gam_metric}()}, \code{\link{maximize_loess_metric}()}, \code{\link{maximize_metric}()}, \code{\link{oc_manual}()}, \code{\link{oc_mean}()}, \code{\link{oc_youden_kernel}()}, \code{\link{oc_youden_normal}()} } \concept{method functions} cutpointr/DESCRIPTION0000644000176200001440000000345014726142632014020 0ustar liggesusersPackage: cutpointr Type: Package Title: Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks Version: 1.2.0 Date: 2024-12-10 Authors@R: person(given = "Christian", family = "Thiele", role = c("cre", "aut"), email = "c.thiele@gmx-topmail.de", comment = c(ORCID = "0000-0002-1156-5117")) Description: Estimate cutpoints that optimize a specified metric in binary classification tasks and validate performance using bootstrapping. Some methods for more robust cutpoint estimation are supported, e.g. a parametric method assuming normal distributions, bootstrapped cutpoints, and smoothing of the metric values per cutpoint using Generalized Additive Models. Various plotting functions are included. For an overview of the package see Thiele and Hirschfeld (2021) . License: GPL-3 URL: https://github.com/thie1e/cutpointr BugReports: https://github.com/thie1e/cutpointr/issues Encoding: UTF-8 LazyData: true Depends: R (>= 3.5.0) LinkingTo: Rcpp Imports: gridExtra (>= 2.2.1), foreach (>= 1.4.3), dplyr (>= 0.8.0), tidyselect (>= 1.1.0), tidyr (>= 1.0.0), purrr (>= 0.3.0), tibble (>= 3.0.0), ggplot2 (>= 3.0.0), Rcpp (>= 0.12.12), stats, utils, rlang (>= 0.4.0) RoxygenNote: 7.3.1 Suggests: KernSmooth (>= 2.23-15), fANCOVA (>= 0.5-1), testthat (>= 1.0.2), doRNG (>= 1.6), doParallel (>= 1.0.11), knitr, rmarkdown, mgcv (>= 1.8), crayon (>= 1.3.4), registry (>= 0.5-1), vctrs (>= 0.2.4) VignetteBuilder: knitr NeedsCompilation: yes Packaged: 2024-12-10 22:15:28 UTC; thic Author: Christian Thiele [cre, aut] () Maintainer: Christian Thiele Repository: CRAN Date/Publication: 2024-12-10 22:50:02 UTC