Given a binary outcome d and continuous measurement m, computes the empirical ROC curve for assessing the classification accuracy of m
StatRocstat_roc(mapping = NULL, data = NULL, geom = "roc",
position = "identity", show.legend = NA, inherit.aes = TRUE,
na.rm = TRUE, max.num.points = 1000, increasing = TRUE, ...)
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.
The geometric object to use display the data
Position adjustment, either as a string, or the result of a call to a position adjustment function.
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.
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
.
Remove missing observations
maximum number of points to plot
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
color = "red"
or size = 3
. They may also be parameters
to the paired geom/stat.
An object of class StatRoc
(inherits from Stat
, ggproto
) of length 5.
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
# NOT RUN {
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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