predict {movieROC}

Predict the classification regions for a particular specificity

Description

This function prints the classification subsets corresponding to a particular false-positive rate FPR or to cutoff value(s) C or XL, XU introduced by the user.

Usage

## S3 method for class 'groc'
predict(object, FPR = NULL, C = NULL, XL = NULL, XU = NULL, ...)
## S3 method for class 'hroc'
predict(object, FPR = 0.15, ...)

Arguments

object

An object of class ‘groc’ or ‘hroc’.

FPR

False-positive rate used to predict the classification region. Default: 0.15 if no cutoff value is provided by the next input parameters.

C

Cutoff value used to predict the classification region for ‘groc’ object with side = "right" or "left". If FPR is provided, C is not used. Default: none.

XL, XU

Cutoff values used to predict the classification region for ‘groc’ object with side = "both" or "both2". If FPR is provided, C is not used. Default: none.

...

Other parameters to be passed. Not used.

Value

A list of length 3 with the following fields:

ClassSubsets

A matrix with the classification region. Number of rows indicate the number of intervals whose union defines the classification region.

Specificity

Resulting specificity value.

Sensitivity

Resulting sensitivity value.

Examples

data(HCC)

roc <- gROC(X = HCC$cg18384097, D = HCC$tumor) # Right-sided ROC curve
predict(roc, FPR = 0.5)
groc <- gROC(X = HCC$cg18384097, D = HCC$tumor, side = "both") # gROC curve
predict(groc, FPR = 0.5)
hroc_cg18384097 <- hROC(X = HCC$cg18384097, D = HCC$tumor, 
    formula.lrm = "D ~ rcs(X,8)") 
predict(hroc_cg18384097, FPR = 0.5)