Description Usage Arguments Value Author(s) Examples
View source: R/bacisThetaPosterior.R
The classification model is conducted based on the BaCIS method and the posterior distribution of θ is returned for further analyses.
1 2 | bacisThetaPosterior(numGroup, tau1, tau2, phi1, phi2,
MCNum, nDat, xDat, 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. |
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. |
The classification model is conducted using the input parameter values and subgroup outcomes. The posterior distribution of θ is returned. The returned value is an matrix in which each column corresponds the data of one subgroup.
Nan Chen and J. Jack Lee / Department of Biostatistics UT MD Anderson Cancer Center
1 2 3 4 5 6 7 8 9 10 11 12 | ## 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)
)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.