plotROC (version 2.3.1)

StatRoc: Calculate the empirical Receiver Operating Characteristic curve

Description

Given a binary outcome d and continuous measurement m, computes the empirical ROC curve for assessing the classification accuracy of m

Usage

StatRoc
stat_roc(
  mapping = NULL,
  data = NULL,
  geom = "roc",
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  na.rm = TRUE,
  max.num.points = 1000,
  increasing = TRUE,
  ...
)

Format

An object of class StatRoc (inherits from Stat, ggproto, gg) of length 6.

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

geom

The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

na.rm

Remove missing observations

max.num.points

maximum number of points to plot

increasing

TRUE (default) if M is positively associated with Pr(D = 1), if FALSE, assumes M is negatively associated with Pr(D = 1)

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

Aesthetics

stat_roc understands the following aesthetics (required aesthetics are in bold):

• m The continuous biomarker/predictor

• d The binary outcome, if not coded as 0/1, the smallest level in sort order is assumed to be 0, with a warning

• alpha Controls the label alpha, see also linealpha and pointalpha

• color

• linetype

• size Controls the line weight, see also pointsize and labelsize

Computed variables

false_positive_fraction

estimate of false positive fraction

true_positive_fraction

estimate of true positive fraction

cutoffs

values of m at which estimates are calculated

Examples

D.ex <- rbinom(50, 1, .5)
rocdata <- data.frame(D = c(D.ex, D.ex), 
                   M = c(rnorm(50, mean = D.ex, sd = .4), rnorm(50, mean = D.ex, sd = 1)), 
                   Z = c(rep("A", 50), rep("B", 50)))

ggplot(rocdata, aes(m = M, d = D)) + stat_roc()