ROCPlot {WVPlots}

Plot receiver operating characteristic plot.

Description

Plot receiver operating characteristic plot.

Usage

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 doi: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

if (requireNamespace('data.table', quietly = TRUE)) {
	# don't multi-thread during CRAN checks
		data.table::setDTthreads(1)
}

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)