Confidence intervals for TPF and FPF are calculated using the exact
method of Clopper and Pearson (1934) each at the level 1 - sqrt(1 -
alpha). Based on result 2.4 from Pepe (2003), the cross-product of these
intervals yields a 1 - alpha
StatRoccistat_rocci(
mapping = NULL,
data = NULL,
geom = "rocci",
position = "identity",
show.legend = NA,
inherit.aes = TRUE,
ci.at = NULL,
sig.level = 0.05,
na.rm = TRUE,
...
)
An object of class StatRocci (inherits from Stat, ggproto, gg) of length 6.
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.
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)).
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 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.
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.
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().
Vector of cutoffs at which to display confidence regions. If NULL, will automatically choose 3 evenly spaced points to display the regions
Significance level for the confidence regions
Remove missing observations
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.
stat_rocci 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
color
fill
linetype
size
estimate of false positive fraction
estimate of true positive fraction
values of m at which estimates are calculated
lower bound of confidence region for FPF
upper bound of confidence region for FPF
lower bound of confidence region for TPF
upper bound of confidence region for TPF
Clopper, C. J., and Egon S. Pearson. "The use of confidence or fiducial limits illustrated in the case of the binomial." Biometrika (1934): 404-413.
Pepe, M.S. "The Statistical Evaluation of Medical Tests for Classification and Prediction." Oxford (2003).
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() + stat_rocci()
ggplot(rocdata, aes(m = M, d = D)) + geom_roc() +
stat_rocci(ci.at = quantile(rocdata$M, c(.1, .3, .5, .7, .9)))
Run the code above in your browser using DataLab