bacisThetaPosterior {bacistool}

Compute the posterior distribution of \theta in the classification model.

Description

The classification model is conducted based on the BaCIS method and the posterior distribution of \theta is returned for further analyses.

Usage

bacisThetaPosterior(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 conducted using the input parameter values and subgroup outcomes. The posterior distribution of \theta is returned. The returned value is an matrix in which each column corresponds the data of one subgroup.

Author(s)

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

Examples

## Conduct subgroup classification and
## compute the posterior distribution of \eqn{\theta}.

library(bacistool)
result<-bacisThetaPosterior(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)
)