GeomRoc: Empirical Receiver Operating Characteristic Curve

Description

Display the empirical ROC curve. Useful for characterizing the classification accuracy of continuous measurements for predicting binary states

Usage

GeomRoc
geom_roc(
  mapping = NULL,
  data = NULL,
  stat = "roc",
  n.cuts = 10,
  arrow = NULL,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  linealpha = 1,
  pointalpha = 1,
  pointsize = 0.5,
  labels = TRUE,
  labelsize = 3.88,
  labelround = 1,
  na.rm = TRUE,
  cutoffs.at = NULL,
  cutoff.labels = NULL,
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Format

An object of class GeomRoc (inherits from Geom, 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)).

stat

Use to override the default connection between geom_roc and stat_roc.

n.cuts

Number of cutpoints to display along each curve

arrow

Arrow specification, as created by arrow

lineend

Line end style (round, butt, square)

linejoin

Line join style (round, mitre, bevel)

linemitre

Line mitre limit (number greater than 1)

linealpha

Alpha level for the lines, alpha.line is deprecated

pointalpha

Alpha level for the cutoff points, alpha.point is deprecated

pointsize

Size of cutoff points, size.point is deprecated

labels

Logical, display cutoff text labels

labelsize

Size of cutoff text labels

labelround

Integer, number of significant digits to round cutoff labels

na.rm

Remove missing values from curve

cutoffs.at

Vector of user supplied cutoffs to plot as points. If non-NULL, it will override the values of n.cuts and plot the observed cutoffs closest to the user-supplied ones.

cutoff.labels

vector of user-supplied labels for the cutoffs. Must be a character vector of the same length as cutoffs.at.

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().

...

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.

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

Aesthetics

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

x The FPF estimate. This is automatically mapped by stat_roc
y The TPF estimate. This is automatically mapped by stat_roc smallest level in sort order is assumed to be 0, with a warning
alpha
color
fill
linetype
size

Examples

Run this code

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)) + geom_roc()
# \donttest{
ggplot(rocdata, aes(m = M, d = D, color = Z)) + geom_roc()
ggplot(rocdata, aes(m = M, d = D)) + geom_roc() + facet_wrap(~ Z)
ggplot(rocdata, aes(m = M, d = D)) + geom_roc(n.cuts = 20)
ggplot(rocdata, aes(m = M, d = D)) + geom_roc(cutoffs.at = c(1.5, 1, .5, 0, -.5))
ggplot(rocdata, aes(m = M, d = D)) + geom_roc(labels = FALSE)
ggplot(rocdata, aes(m = M, d = D)) + geom_roc(size = 1.25)
# }

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