summary.cROC {ROCnReg} | R Documentation |
Summary method for cROC
objects
Description
Default summary method for objects fitted with cROC.bnp()
, cROC.sp()
, or cROC.kernel()
functions.
Usage
## S3 method for class 'cROC'
summary(object, ...)
Arguments
object |
An object of class |
... |
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, and the sample sizes are printed. For the semiparametric approach (cROC.sp()
), the estimated coefficients (and 95% confidence intervals, if required) of the model for the healthy population, the diseased population and the conditional ROC curve, are printed. In addition, the function provides the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). For the nonparametric Bayesian approach (cROC.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 both healthy and diseased populations). For the kernel-based approach (cROC.kernel()
), information regarding the selected bandwidths and the type of kernel estimator(for both healthy and diseased populations and for both regression and variance functions) is printed.
References
Rodriguez-Alvarez, M.X., Tahoces, P.G., Cadarso-Suarez, C. and Lado, M.J. (2011). Comparative study of ROC regression techniques. Applications for the computer-aided diagnostic system in breast cancer detection. Computational Statistics and Data Analysis, 55, 888–902.
See Also
cROC.bnp
, cROC.sp
or cROC.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)
cROC_bnp <- cROC.bnp(formula.h = l_marker1 ~ f(age, K = 0),
formula.d = l_marker1 ~ f(age, K = 0),
group = "status",
tag.h = 0,
data = newpsa,
standardise = TRUE,
p = seq(0, 1, len = 101),
compute.lpml = TRUE,
compute.WAIC = TRUE,
compute.DIC = TRUE,
pauc = pauccontrol(compute = TRUE, value = 0.5, focus = "FPF"),
density = densitycontrol(compute = TRUE, grid.h = NA, grid.d = NA),
mcmc = mcmccontrol(nsave = 500, nburn = 100, nskip = 1))
summary(cROC_bnp)