mcmccontrol: Markov chain Monte Carlo (MCMC) parameters

In ROCnReg: ROC Curve Inference with and without Covariates

Markov chain Monte Carlo (MCMC) parameters

Description

This function is used to set various parameters controlling the Markov chain Monte Carlo (MCMC) parameters.

Usage

mcmccontrol(nsave = 8000, nburn = 2000, nskip = 1)

Arguments

nsave	An integer giving the total number of scans to be saved (does not include the burn-in and thinning iterations).
nburn	An integer giving the number of burn-in scans.
nskip	An integer giving the thinning interval,

Details

The value returned by this function is used as a control argument of the AROC.bnp, cROC.bnp, and pooledROC.dpm functions.

Value

A list with components for each of the possible arguments.

See Also

pooledROC.dpm, AROC.bnp and cROC.bnp

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))

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