bacisPlotClassification {bacistool} | R Documentation |
The classification model is conducted based on the BaCIS method and the posterior density of θ is plotted.
bacisPlotClassification(numGroup, tau1, tau2, phi1, phi2, clusterCutoff, MCNum, nDat, xDat, cols, seed)
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. |
The classification model is conducted using the input parameter values and subgroup outcomes. The posterior density of θ is plotted.
Nan Chen and J. Jack Lee / Department of Biostatistics UT MD Anderson Cancer Center
## 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") )