Plot receiver operating characteristic plot.

ROCPlot(frame, xvar, truthVar, truthTarget, title, ...,
  estimate_sig = FALSE, returnScores = FALSE, nrep = 100,
  parallelCluster = NULL)

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.

Details

See http://www.nature.com/nmeth/journal/v13/n8/full/nmeth.3945.html for a discussion of true positive and false positive rates, and how the ROC plot relates to the precision/recall plot.

See also

Examples

set.seed(34903490) x = rnorm(50) y = 0.5*x^2 + 2*x + rnorm(length(x)) frm = data.frame(x=x,yC=y>=as.numeric(quantile(y,probs=0.8))) WVPlots::ROCPlot(frm, "x", "yC", TRUE, title="Example ROC plot", estimate_sig = TRUE)