NPflow (version 0.9.0)

DPMGibbsSkewT_SeqPrior_parallel: Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions

Description

Slice Sampling of Dirichlet Process Mixture of skew Students's t-distibutions

Usage

DPMGibbsSkewT_SeqPrior_parallel(Ncpus, type_connec, z, prior_inform, hyperG0, N,
  nbclust_init, add.vagueprior = TRUE, weightnoninfo = NULL,
  doPlot = FALSE, plotevery = N/10, diagVar = TRUE, verbose = TRUE,
  monitorfile = "", ...)

Arguments

Ncpus

the number of processors available

type_connec

The type of connection between the processors. Supported cluster types are "SOCK", "FORK", "MPI", and "NWS". See also makeCluster.

z

data matrix d x n with d dimensions in rows and n observations in columns.

prior_inform

an informative prior such as the approximation computed by summary.DPMMclust.

hyperG0

prior mixing distribution.

N

number of MCMC iterations.

nbclust_init

number of clusters at initialisation. Default to 30 (or less if there are less than 30 observations).

add.vagueprior

logical flag indicating wether a non informative component should be added to the informative prior. Default is TRUE.

weightnoninfo

a real between 0 and 1 giving the weights of the non informative component in the prior.

doPlot

logical flag indicating wether to plot MCMC iteration or not. Default to TRUE.

plotevery

an integer indicating the interval between plotted iterations when doPlot is TRUE.

diagVar

logical flag indicating wether the variance of each cluster is estimated as a diagonal matrix, or as a full matrix. Default is TRUE (diagonal variance).

verbose

logical flag indicating wether partition info is written in the console at each MCMC iteration.

monitorfile

a writable connections or a character string naming a file to write into, to monitor the progress of the analysis. Default is "" which is no monitoring. See Details.

...

additional arguments to be passed to plot_DPM. Only used if doPlot is TRUE.

Value

• a object of class DPMclust with the following attributes:
  • mcmc_partitions:
  {a list of length N. Each element mcmc_partitions[n] is a vector of length n giving the partition of the n observations.}
• alpha:
  a vector of length N. cost[j] is the cost associated to partition c[[j]]
• U_SS_list:
  a list of length N containing the lists of sufficient statistics for all the mixture components at each MCMC iteration
• weights_list:
  a list of length N containing the logposterior values at each MCMC iterations
• logposterior_list:
  a list of length N containing the logposterior values at each MCMC iterations
• data:
  the data matrix d x n with d dimensions in rows and n observations in columns
• nb_mcmcit:
  the number of MCMC itertations
• clust_distrib:
  the parametric distribution of the mixture component - "skewt"
• hyperG0:
  the prior on the cluster location

References

Hejblum BP, Alkhassim C, Gottardo R, Caron F, Thiebaut R, Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data, in preparation.

Examples

rm(list=ls())

#Number of data
n <- 2000
set.seed(123)


d <- 2
ncl <- 4

# Sample data

sdev <- array(dim=c(d,d,ncl))

#xi <- matrix(nrow=d, ncol=ncl, c(-1.5, 1.5, 1.5, 1.5, 2, -2.5, -2.5, -3))
#psi <- matrix(nrow=d, ncol=4, c(0.4, -0.6, 0.8, 0, 0.3, -0.7, -0.3, -0.8))
xi <- matrix(nrow=d, ncol=ncl, c(-0.2, 0.5, 2.4, 0.4, 0.6, -1.3, -0.9, -2.7))
psi <- matrix(nrow=d, ncol=4, c(0.3, -0.7, -0.8, 0, 0.3, -0.7, 0.2, 0.9))
nu <- c(100,15,8,5)
p <- c(0.15, 0.05, 0.5, 0.3) # frequence des clusters
sdev[, ,1] <- matrix(nrow=d, ncol=d, c(0.3, 0, 0, 0.3))
sdev[, ,2] <- matrix(nrow=d, ncol=d, c(0.1, 0, 0, 0.3))
sdev[, ,3] <- matrix(nrow=d, ncol=d, c(0.3, 0.15, 0.15, 0.3))
sdev[, ,4] <- .3*diag(2)


c <- rep(0,n)
w <- rep(1,n)
z <- matrix(0, nrow=d, ncol=n)
for(k in 1:n){
 c[k] = which(rmultinom(n=1, size=1, prob=p)!=0)
 w[k] <- rgamma(1, shape=nu[c[k]]/2, rate=nu[c[k]]/2)
 z[,k] <- xi[, c[k]] + psi[, c[k]]*rtruncnorm(n=1, a=0, b=Inf, mean=0, sd=1/sqrt(w[k])) +
                (sdev[, , c[k]]/sqrt(w[k]))%*%matrix(rnorm(d, mean = 0, sd = 1), nrow=d, ncol=1)
 #cat(k, "/", n, " observations simulated\\n", sep="")
}

# Set parameters of G0
hyperG0 <- list()
hyperG0[["b_xi"]] <- rowMeans(z)
hyperG0[["b_psi"]] <- rep(0,d)
hyperG0[["kappa"]] <- 0.001
hyperG0[["D_xi"]] <- 100
hyperG0[["D_psi"]] <- 100
hyperG0[["nu"]] <- d+1
hyperG0[["lambda"]] <- diag(apply(z,MARGIN=1, FUN=var))/3

 # hyperprior on the Scale parameter of DPM
 a <- 0.0001
 b <- 0.0001

 # do some plots
 nbclust_init <- 30

 ## Plot Data
 library(ggplot2)
 q <- (ggplot(data.frame("X"=z[1,], "Y"=z[2,]), aes(x=X, y=Y))
       + geom_point()
       + ggtitle("Simple example in 2d data")
       +xlab("D1")
       +ylab("D2")
       +theme_bw())
 q

MCMCsample_st <- DPMGibbsSkewT(z, hyperG0, a, b, N=2000,
                                doPlot=TRUE, plotevery=250,
                                nbclust_init,
                                gg.add=list(theme_bw(),
                                 guides(shape=guide_legend(override.aes = list(fill="grey45")))),
                                diagVar=FALSE)
 s <- summary(MCMCsample_st, burnin = 1500, thin=5, posterior_approx=TRUE)
 F <- FmeasureC(pred=s$point_estim$c_est, ref=c)

for(k in 1:n){
 c[k] = which(rmultinom(n=1, size=1, prob=p)!=0)
 w[k] <- rgamma(1, shape=nu[c[k]]/2, rate=nu[c[k]]/2)
 z[,k] <- xi[, c[k]] + psi[, c[k]]*rtruncnorm(n=1, a=0, b=Inf, mean=0, sd=1/sqrt(w[k])) +
                (sdev[, , c[k]]/sqrt(w[k]))%*%matrix(rnorm(d, mean = 0, sd = 1), nrow=d, ncol=1)
 #cat(k, "/", n, " observations simulated\n", sep="")
}
MCMCsample_st2 <- DPMGibbsSkewT_SeqPrior_parallel(Ncpus=2, type_connec="SOCK",
                                                  z, prior_inform=s$param_posterior,
                                                  hyperG0, N=3000,
                                                  doPlot=TRUE, plotevery=100,
                                                  nbclust_init, diagVar=FALSE, verbose=FALSE,
                                                  gg.add=list(theme_bw(),
                                 guides(shape=guide_legend(override.aes = list(fill="grey45")))))
s2 <- summary(MCMCsample_st2, burnin = 2000, thin=5)
F2 <- FmeasureC(pred=s2$point_estim$c_est, ref=c)