summary.AROC: Summary method for 'AROC' objects

In ROCnReg: ROC Curve Inference with and without Covariates

Summary method for AROC objects

Description

Default summary method for objects fitted with AROC.bnp(), AROC.sp(), or AROC.kernel() functions.

Usage

## S3 method for class 'AROC'
summary(object, ...)

Arguments

object	An object of class AROC as produced by AROC.bnp(), AROC.sp(), or AROC.kernel().
...	Further arguments passed to or from other methods. Not yet implemented.

Details

The information printed depends on the method. In all cases, the call to the function, the method, the area under the covariate-adjusted ROC curve (AAUC), the partial area under the covariate-adjusted ROC curve (if required) (AAUC), and the sample sizes are printed. For the semiparametric approach (AROC.sp()), the estimated coefficients (and 95% confidence intervals, if required) of the model for the healthy population are printed. In addition, the function provides the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). For the nonparametric Bayesian approach (AROC.bnp()), and if required, the function provides the log pseudo marginal likelihood (LPML), the widely applicable information criterion (WAIC) and/or the deviance information criterion (DIC). For the kernel-based approach (AROC.kernel()), information regarding the selected bandwidth and the type of kernel estimator (for both regression and variance functions) is printed.

See Also

AROC.bnp, AROC.sp or AROC.kernel.

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)

m0 <- AROC.bnp(formula.h = l_marker1 ~ f(age, K = 0),
group = "status", tag.h = 0, data = newpsa, standardise = TRUE,
p = seq(0,1,l=101), compute.lpml = TRUE, compute.WAIC = TRUE)

summary(m0)

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