summary.DPMMclust: Summarizing Dirichlet Process Mixture Models

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

Summarizing Dirichlet Process Mixture Models

Description

Summary methods for DPMMclust objects.

Usage

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

Arguments

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

Details

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

Value

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

Author(s)

Boris Hejblum

See Also

similarityMat similarityMatC

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