geom_rocci: Confidence regions for the ROC curve

In plotROC: Generate Useful ROC Curve Charts for Print and Interactive Use

Confidence regions for the ROC curve

Description

Display rectangular confidence regions for the empirical ROC curve.

Usage

geom_rocci(
  mapping = NULL,
  data = NULL,
  stat = "rocci",
  ci.at = NULL,
  sig.level = 0.05,
  na.rm = TRUE,
  alpha.box = 0.3,
  labels = TRUE,
  labelsize = 3.88,
  labelround = 1,
  position = "identity",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

GeomRocci

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_rocci and stat_rocci.
ci.at	Vector of values in the range of the biomarker where confidence regions will be displayed
sig.level	Significance level for the confidence regions
na.rm	If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.
alpha.box	Alpha level for the confidence regions
labels	If TRUE, adds text labels for the cutoffs where the confidence regions are displayed
labelsize	Size of cutoff text labels
labelround	Integer, number of significant digits to round cutoff labels
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.

Format

An object of class GeomRocci (inherits from Geom, ggproto, gg) of length 6.

Aesthetics

geom_rocci understands the following aesthetics (required aesthetics are in bold). stat_rocci automatically maps the estimates to the required aesthetics:

• x The FPF estimate

• y The TPF estimate

• xmin Lower confidence limit for the FPF

• xmax Upper confidence limit for the FPF

• ymin Lower confidence limit for the TPF

• ymax Upper confidence limit for the TPF

• alpha

• color

• fill

• linetype

• size

See Also

See geom_roc for the empirical ROC curve, style_roc for adding guidelines and labels, and direct_label for adding direct labels to the curves. Also export_interactive_roc for creating interactive ROC curve plots for use in a web browser.

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)) + geom_roc() + geom_rocci()
ggplot(rocdata, aes(m = M, d = D, color = Z)) + geom_roc() + geom_rocci()
ggplot(rocdata, aes(m = M, d = D, color = Z)) + geom_roc() + geom_rocci(sig.level = .01)
ggplot(rocdata, aes(m = M, d = D)) + geom_roc(n.cuts = 0) + 
geom_rocci(ci.at = quantile(rocdata$M, c(.1, .25, .5, .75, .9)))
ggplot(rocdata, aes(m = M, d = D, color = Z)) + geom_roc() + geom_rocci(linetype = 1)

plotROC documentation built on Oct. 6, 2023, 5:10 p.m.