NPflow (version 0.13.5)

rCRP: Generating cluster data from the Chinese Restaurant Process

Description

Generating cluster data from the Chinese Restaurant Process

Usage

rCRP(n = 1000, alpha = 2, hyperG0, verbose = TRUE)

Arguments

n

number of observations

alpha

concentration parameter

hyperG0

base distribution hyperparameter

verbose

logical flag indicating whether info is written in the console.

Examples

rm(list=ls())

d=2
hyperG0 <- list()
hyperG0[["NNiW"]] <- list()
hyperG0[["NNiW"]][["b_xi"]] <- rep(0,d)
hyperG0[["NNiW"]][["b_psi"]] <- rep(0,d)
hyperG0[["NNiW"]][["D_xi"]] <- 100
hyperG0[["NNiW"]][["D_psi"]] <- 8
hyperG0[["NNiW"]][["nu"]] <- d+1
hyperG0[["NNiW"]][["lambda"]] <- diag(c(1,1))

hyperG0[["scale"]] <- list()

set.seed(4321)
N <- 200
alph <- runif(n=1,0.2,2)
GvHD_sims <- rCRP(n=2*N, alpha=alph, hyperG0=hyperG0)
library(ggplot2)
q <- (ggplot(data=cbind.data.frame("D1"=GvHD_sims$data[1,],
                                  "D2"=GvHD_sims$data[2,],
                                  "Cluster"=GvHD_sims$cluster),
             aes(x=D1, y=D2))
      + geom_point(aes(colour=Cluster), alpha=0.6)
      + theme_bw()
      )
q
#q + stat_density2d(alpha=0.15, geom="polygon")

if(interactive()){
MCMCy1 <- DPMGibbsSkewT(z=GvHD_sims$data[,1:N],
                        hyperG0$NNiW, a=0.0001, b=0.0001, N=5000,
                        doPlot=TRUE, nbclust_init=64, plotevery=500,
                        gg.add=list(theme_bw()), diagVar=FALSE)
 s1 <- summary(MCMCy1, burnin=4000, thin=5,
               posterior_approx=TRUE)
 F1 <- FmeasureC(ref=GvHD_sims$cluster[1:N], pred=s1$point_estim$c_est)

 # s <- summary(MCMCy1, burnin=4000, thin=5,
 #               posterior_approx=TRUE, K=1)
 # s2 <- summary(MCMCy1, burnin=4000, thin=5,
 #               posterior_approx=TRUE, K=2)
 # MCMCy2_seqPost<- DPMGibbsSkewT(z=GvHD_sims$data[,(N+1):(2*N)],
 #                                  hyperG0=s1$param_post$parameters,
 #                                  a=s1$param_post$alpha_param$shape,
 #                                  b=s1$param_post$alpha_param$rate,
 #                                  N=5000, doPlot=TRUE, nbclust_init=64, plotevery=500,
 #                                  gg.add=list(theme_bw()), diagVar=FALSE)

 MCMCy2_seqPost <- DPMGibbsSkewT_SeqPrior(z=GvHD_sims$data[,(N+1):(2*N)],
                                           prior=s1$param_post, hyperG0=hyperG0$NNiW, , N=1000,
                                           doPlot=TRUE, nbclust_init=10, plotevery=100,
                                           gg.add=list(theme_bw()), diagVar=FALSE)
 s2_seqPost <- summary(MCMCy2_seqPost, burnin=600, thin=2)
 F2_seqPost <- FmeasureC(ref=GvHD_sims$cluster[(N+1):(2*N)], pred=s2_seqPost$point_estim$c_est)

 MCMCy2 <- DPMGibbsSkewT(z=GvHD_sims$data[,(N+1):(2*N)],
                         hyperG0$NNiW, a=0.0001, b=0.0001, N=5000,
                         doPlot=TRUE, nbclust_init=64, plotevery=500,
                         gg.add=list(theme_bw()), diagVar=FALSE)
 s2 <- summary(MCMCy2, burnin=4000, thin=5)
 F2 <- FmeasureC(ref=GvHD_sims$cluster[(N+1):(2*N)], pred=s2$point_estim$c_est)

 MCMCtot <- DPMGibbsSkewT(z=GvHD_sims$data,
                          hyperG0$NNiW, a=0.0001, b=0.0001, N=5000,
                          doPlot=TRUE, nbclust_init=10, plotevery=500,
                          gg.add=list(theme_bw()), diagVar=FALSE)
 stot <- summary(MCMCtot, burnin=4000, thin=5)
 F2tot <- FmeasureC(ref=GvHD_sims$cluster[(N+1):(2*N)], pred=stot$point_estim$c_est[(N+1):(2*N)])

 c(F1, F2, F2_seqPost, F2tot)
}