bacisCheckDIC: Compute the DIC value for the classification model.

In bacistool: Bayesian Classification and Information Sharing (BaCIS) Tool for the Design of Multi-Group Phase II Clinical Trials

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
                      )

bacistool documentation built on July 1, 2020, 10:24 p.m.