cROCData: Selects an adequate set of points from a data set for...

In ROCnReg: ROC Curve Inference with and without Covariates

Selects an adequate set of points from a data set for obtaining predictions.

Description

Selects an adequate set of points from a data set for obtaining predictions

Usage

cROCData(data, names.cov, group)

Arguments

data	Data set from which the new set of covariate values is obtained.
names.cov	Character vector with the names of the covariates to be included in the new data set.
group	A character string with the name of the variable in the original data set that distinguishes healthy from diseased individuals.

Value

A data frame containing selected values of all needed covariates. For those that are continuous, 30 different values are selected.

See Also

AROC.bnp, cROC.bnp, cROC.sp, cROC.kernel, compute.threshold.cROC or compute.threshold.AROC.

Examples

library(ROCnReg)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]

# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)

newdf <- cROCData(newpsa, "age", "status")

summary(newdf)

ROCnReg documentation built on March 31, 2023, 5:42 p.m.