bacisPlotClassification {bacistool} R Documentation

## Plot the posterior density of θ in the classification model.

### Description

The classification model is conducted based on the BaCIS method and the posterior density of θ is plotted.

### Usage


bacisPlotClassification(numGroup, tau1, tau2, phi1, phi2,
clusterCutoff, MCNum, nDat, xDat, cols, 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. clusterCutoff The cutoff value of the cluster classification. If its value is NA, adaptive classification is applied. 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. cols The color vector of all subgroups in the illustration. 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 density of θ is plotted.

### Author(s)

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

### Examples


## Compute the posterior distribution of \eqn{\theta}.
library(bacistool)
bacisPlotClassification(numGroup=5,
tau1=NA,
tau2=.001,
phi1=0.1, phi2=0.3,
clusterCutoff=NA,
MCNum=5000,
nDat=c(25,25,25,25,25),
xDat=c(3,4,3,8,7),
cols = c("brown", "red", "orange", "blue", "green")

)



[Package bacistool version 1.0.0 Index]