Summary methods for DPMMclust objects.
# S3 method for DPMMclust
summary(
object,
burnin = 0,
thin = 1,
gs = NULL,
lossFn = "F-measure",
posterior_approx = FALSE,
...
)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
a DPMMclust object.
integer giving the number of MCMC iterations to burn (defaults is half)
integer giving the spacing at which MCMC iterations are kept.
Default is 1, i.e. no thining.
optional vector of length n containing the gold standard
partition of the n observations to compare to the point estimate
character string specifying the loss function to be used. Either "F-measure" or "Binder" (see Details). Default is "F-measure".
logical flag whether a parametric approximation of the posterior should be
computed. Default is FALSE
further arguments passed to or from other methods
Boris Hejblum
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
similarityMat similarityMatC