Summarizing Dirichlet Process Mixture Models

Summary methods for DPMMclust objects.

Usage

# S3 method for class 'DPMMclust'
summary(
  object,
  burnin = 0,
  thin = 1,
  gs = NULL,
  lossFn = "Binder",
  posterior_approx = FALSE,
  ...
)

object: a DPMMclust object.
burnin: integer giving the number of MCMC iterations to burn (defaults is half)
thin: integer giving the spacing at which MCMC iterations are kept. Default is 1, i.e. no thining.
gs: optional vector of length n containing the gold standard partition of the n observations to compare to the point estimate
lossFn: character string specifying the loss function to be used. Either "F-measure" or "Binder" (see Details). Default is "Binder".
posterior_approx: logical flag whether a parametric approximation of the posterior should be computed. Default is FALSE
...: further arguments passed to or from other methods

a list containing the following elements:

nb_mcmcit:

an integer giving the value of m, the number of retained sampled partitions, i.e. (N - burnin)/thin

burnin:

an integer passing along the burnin argument

thin:

an integer passing along the thin argument

lossFn:

a character string passing along the lossFn argument

clust_distrib:

a character string passing along the clust_distrib argument

point_estim:

a list containing:

c_est:: a vector of length ncontaining the point estimated clustering for each observations
cost:: a vector of length m containing the cost of each sampled partition
Fmeas:: if lossFn is 'F-measure', the m x m matrix of total F-measures for each pair of sampled partitions
opt_ind:: the index of the point estimate partition among the m sampled

loss:

the loss for the point estimate. NA if lossFn is not 'Binder'

param_posterior:

a list containing the parametric approximation of the posterior, suitable to be plugged in as prior for a new MCMC algorithm run

mcmc_partitions:

a list containing the m sampled partitions

alpha:

a vector of length m with the values of the alpha DP parameter

index_estim:

the index of the point estimate partition among the m sampled

hyperG0:

a list passing along the prior, i.e. the hyperG0 argument

logposterior_list:

a list of length m containing the logposterior and its decomposition, for each sampled partition

U_SS_list:

a list of length m containing the containing the lists of sufficient statistics for all the mixture components, for each sampled partition

data:

a d x n matrix containing the clustered data

The cost of a point estimate partition is calculated using either a pairwise coincidence loss function (Binder), or 1-Fmeasure (F-measure).

The number of retained sampled partitions is m = (N - burnin)/thin

Boris Hejblum