Plot receiver operating characteristic plot.

Plot receiver operating characteristic plot.

ROCPlot(
  frame,
  xvar,
  truthVar,
  truthTarget,
  title,
  ...,
  estimate_sig = FALSE,
  returnScores = FALSE,
  nrep = 100,
  parallelCluster = NULL,
  curve_color = "darkblue",
  fill_color = "black",
  diag_color = "black",
  add_beta_ideal_curve = FALSE,
  beta_ideal_curve_color = "#fd8d3c",
  add_beta1_ideal_curve = FALSE,
  beta1_ideal_curve_color = "#f03b20",
  add_symmetric_ideal_curve = FALSE,
  symmetric_ideal_curve_color = "#bd0026",
  add_convex_hull = FALSE,
  convex_hull_color = "#404040",
  ideal_plot_step_size = 0.001
)

Arguments

frame	data frame to get values from
xvar	name of the independent (input or model) column in frame
truthVar	name of the dependent (output or result to be modeled) column in frame
truthTarget	value we consider to be positive
title	title to place on plot
...	no unnamed argument, added to force named binding of later arguments.
estimate_sig	logical, if TRUE estimate and display significance of difference from AUC 0.5.
returnScores	logical if TRUE return detailed permutedScores
nrep	number of permutation repetitions to estimate p values.
parallelCluster	(optional) a cluster object created by package parallel or package snow.
curve_color	color of the ROC curve
fill_color	shading color for the area under the curve
diag_color	color for the AUC=0.5 line (x=y)
add_beta_ideal_curve	logical, if TRUE add the beta(a, b), beta(c, d) ideal curve found by moment matching.
beta_ideal_curve_color	color for ideal curve.
add_beta1_ideal_curve	logical, if TRUE add the beta(1, a), beta(b, 2) ideal curve defined in https://journals.sagepub.com/doi/abs/10.1177/0272989X15582210
beta1_ideal_curve_color	color for ideal curve.
add_symmetric_ideal_curve	logical, if TRUE add the ideal curve as discussed in https://win-vector.com/2020/09/13/why-working-with-auc-is-more-powerful-than-one-might-think/.
symmetric_ideal_curve_color	color for ideal curve.
add_convex_hull	logical, if TRUE add convex hull to plot
convex_hull_color	color for convex hull curve
ideal_plot_step_size	step size used in ideal plots

Details

See https://www.nature.com/articles/nmeth.3945 for a discussion of true positive and false positive rates, and how the ROC plot relates to the precision/recall plot.

See also

PRTPlot, ThresholdPlot

Examples

beta_example <- function(
  n,
  shape1_pos, shape2_pos,
  shape1_neg, shape2_neg) {
  d <- data.frame(
    y = sample(
      c(TRUE, FALSE),
      size = n,
      replace = TRUE),
    score = 0.0
  )
  d$score[d$y] <- rbeta(sum(d$y), shape1 = shape1_pos, shape2 = shape2_pos)
  d$score[!d$y] <- rbeta(sum(!d$y), shape1 = shape1_neg, shape2 = shape2_neg)
  d
}

d1 <- beta_example(
  100,
  shape1_pos = 6,
  shape2_pos = 5,
  shape1_neg = 1,
  shape2_neg = 2)

ROCPlot(
   d1,
   xvar = "score",
   truthVar = "y", truthTarget = TRUE,
   title="Example ROC plot",
   estimate_sig = TRUE,
   add_beta_ideal_curve = TRUE,
   add_convex_hull = TRUE)