bacisPlotClassification: Plot the posterior density of theta in the classification...

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

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")

)

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