bacisPlotClassification {bacistool} | R Documentation |
\theta
in the classification model.
The classification model is conducted based on the BaCIS method and the posterior density of \theta
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 \theta
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")
)