View source: R/summary.DPMMclust.R
summary.DPMMclust | R Documentation |
Summary methods for DPMMclust
objects.
## S3 method for class 'DPMMclust'
summary(
object,
burnin = 0,
thin = 1,
gs = NULL,
lossFn = "Binder",
posterior_approx = FALSE,
...
)
object |
a |
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 |
gs |
optional vector of length |
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 |
... |
further arguments passed to or from other methods |
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
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 n
containing 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
Boris Hejblum
similarityMat
similarityMatC
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