StatRoc | R Documentation |
Given a binary outcome d and continuous measurement m, computes the empirical ROC curve for assessing the classification accuracy of m
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,
...
)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
geom |
The geometric object to use to display the data, either as a
|
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
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 |
An object of class StatRoc
(inherits from Stat
, ggproto
, gg
) of length 6.
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
estimate of false positive fraction
estimate of true positive fraction
values of m at which estimates are calculated
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()
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