StatRocci | R Documentation |
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
StatRocci
stat_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,
...
)
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 |
ci.at |
Vector of cutoffs at which to display confidence regions. If NULL, will automatically choose 3 evenly spaced points to display the regions |
sig.level |
Significance level for the confidence regions |
na.rm |
Remove missing observations |
... |
Other arguments passed on to |
An object of class StatRocci
(inherits from Stat
, ggproto
, gg
) of length 6.
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)))
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