bacisCheckDIC {bacistool}

Compute the DIC value for the classification model.

Description

In this function, the classification model is applied using the input parameter values and the DIC value is calculated.

Usage

bacisCheckDIC(numGroup, tau1, tau2, phi1, phi2,
               MCNum, nDat, xDat, seed)

Arguments

numGroup

Number of subgroups in the trial.

tau1

The precision parameter of subgroups clustering for the classification model.

tau2

The precision prior for the latent variable for the classification.

phi1

Center for the low response rate cluster.

phi2

Center for the high response rate cluster.

MCNum

The number of MCMC sampling iterations.

nDat

The vector of total sample sizes of all subgroups.

xDat

The vector of the response numbers of all subgroups.

seed

Random seed value. If its value is NA, a time dependent random seed is generated and applied.

Value

The classification model is applied using the input parameter values and the DIC value is returned.

Author(s)

Nan Chen and J. Jack Lee / Department of Biostatistics UT MD Anderson Cancer Center

Examples

## An example to compute the DIC value.
library(bacistool)
result<-bacisCheckDIC(numGroup=5,
                      tau1=NA,
                      tau2=.001,
                      phi1=0.1, phi2=0.3,
                      MCNum=5000,
                      nDat=c(25,25,25,25,25),
                      xDat=c(3,4,3,8,7),
                      seed=100
                      )