DPMGibbsSkewT_SeqPrior: Slice Sampling of Dirichlet Process Mixture of skew Student's...

In NPflow: Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry Data

Slice Sampling of Dirichlet Process Mixture of skew Student's t-distributions

Description

Slice Sampling of Dirichlet Process Mixture of skew Student's t-distributions

Usage

DPMGibbsSkewT_SeqPrior(
  z,
  prior_inform,
  hyperG0,
  N,
  nbclust_init,
  add.vagueprior = TRUE,
  weightnoninfo = NULL,
  doPlot = TRUE,
  plotevery = N/10,
  diagVar = TRUE,
  verbose = TRUE,
  ...
)

Arguments

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 initialization. Default to 30 (or less if there are less than 30 observations).
add.vagueprior	logical flag indicating whether 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 whether 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 whether 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 whether partition info is written in the console at each MCMC iteration.
...	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 weights of each mixture component for 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 iterations
clust_distrib:	the parametric distribution of the mixture component - "skewt"
hyperG0:	the prior on the cluster location

Author(s)

Boris Hejblum

References

Hejblum BP, Alkhassim C, Gottardo R, Caron F and Thiebaut R (2019) Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data. The Annals of Applied Statistics, 13(1): 638-660. <doi: 10.1214/18-AOAS1209> <arXiv: 1702.04407> https://arxiv.org/abs/1702.04407 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/18-AOAS1209")}

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, 1.5, 1, 1.5, -2, -2, -2))
psi <- matrix(nrow=d, ncol=4, c(0.4, -0.6, 0.8, 0, 0.3, -0.7, -0.3, -0.8))
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

if(interactive()){
 MCMCsample_st <- DPMGibbsSkewT(z, hyperG0, a, b, N=2000,
                                doPlot=TRUE, plotevery=250,
                                nbclust_init, diagVar=FALSE,
                                gg.add=list(theme_bw(),
                                 guides(shape=guide_legend(override.aes = list(fill="grey45")))))
 s <- summary(MCMCsample_st, burnin = 1500, thin=2, 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(z, prior=s$param_posterior,
                                          hyperG0, N=2000,
                                          doPlot=TRUE, plotevery=100,
                                          nbclust_init, diagVar=FALSE,
                                          gg.add=list(theme_bw(),
                                 guides(shape=guide_legend(override.aes = list(fill="grey45")))))
s2 <- summary(MCMCsample_st2, burnin = 1500, thin=5)
F2 <- FmeasureC(pred=s2$point_estim$c_est, ref=c)

# MCMCsample_st2_par <- DPMGibbsSkewT_SeqPrior_parallel(Ncpus= 2, type_connec="SOCK",
#                                                       z, prior_inform=s$param_posterior,
#                                                       hyperG0, N=2000,
#                                                       doPlot=TRUE, plotevery=50,
#                                                       nbclust_init, diagVar=FALSE,
#                                                       gg.add=list(theme_bw(),
#                                  guides(shape=guide_legend(override.aes = list(fill="grey45"))))
}

NPflow documentation built on Aug. 21, 2025, 5:53 p.m.