NPflow (version 0.9.0)

DPMGibbsN_parallel: Slice Sampling of the Dirichlet Process Mixture Model with a prior on alpha

Description

Slice Sampling of the Dirichlet Process Mixture Model with a prior on alpha

Usage

DPMGibbsN_parallel(Ncpus, type_connec, z, hyperG0, a, b, N, doPlot = TRUE,
  nbclust_init = 30, 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.

hyperG0

prior mixing distribution.

a

shape hyperparameter of the Gamma prior on the parameter of the Dirichlet Process.

b

scale hyperparameter of the Gamma prior on the parameter of the Dirichlet Process.

N

number of MCMC iterations.

doPlot

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

nbclust_init

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

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]]
• listU_mu:
  a list of length N containing the matrices of mean vectors for all the mixture components at each MCMC iteration
• listU_Sigma:
  a list of length N containing the arrays of covariances matrices for all the mixture components at each MCMC iteration
• 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 - "gaussian"
• hyperG0:
  the prior on the cluster location

See Also

DPMGibbsN

Examples

# Scaling up: ----
rm(list=ls())
#Number of data
n <- 2000
set.seed(1234)

# Sample data
d <- 10
nclust <- 20
m <- matrix(nrow=d, ncol=nclust, runif(d*nclust)*8)
# p: cluster probabilities
p <- runif(nclust)
p <- p/sum(p)

# Covariance matrix of the clusters
sdev <- array(dim=c(d, d, nclust))
for (j in 1:nclust){
    sdev[, ,j] <- matrix(NA, nrow=d, ncol=d)
    diag(sdev[, ,j]) <- abs(rnorm(n=d, mean=0.3, sd=0.1))
    sdev[, ,j][lower.tri(sdev[, ,j], diag = FALSE)] <- rnorm(n=d*(d-1)/2,
    mean=0, sd=0.05)
    sdev[, ,j][upper.tri(sdev[, ,j], diag = FALSE)] <- (sdev[, ,j][
                                                        lower.tri(sdev[, ,j], diag = FALSE)])
}
c <- rep(0,n)
z <- matrix(0, nrow=d, ncol=n)
for(k in 1:n){
    c[k] = which(rmultinom(n=1, size=1, prob=p)!=0)
    z[,k] <- m[, c[k]] + sdev[, , c[k]]%*%matrix(rnorm(d, mean = 0, sd = 1), nrow=d, ncol=1)
    #cat(k, "/", n, " observations simulated\\n", sep="")
}

# hyperprior on the Scale parameter of DPM
a <- 0.001
b <- 0.001

# Number of iterations
N <- 25

# do some plots
doPlot <- TRUE

# Set parameters of G0
hyperG0 <- list()
hyperG0[["mu"]] <- rep(0, d)
hyperG0[["kappa"]] <- 0.01
hyperG0[["nu"]] <- d + 2
hyperG0[["lambda"]] <- diag(d)/10


nbclust_init <- 30