NMixCluster: Clustering based on the MCMC output of the mixture model

In mixAK: Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering

Clustering based on the MCMC output of the mixture model

Description

TO BE ADDED.

This function only works for models with a fixed number of mixture components.

Usage

NMixCluster(object, ...)

## Default S3 method:
NMixCluster(object, ...)

## S3 method for class 'GLMM_MCMC'
NMixCluster(object,
   prob = c("poster.comp.prob", "quant.comp.prob", "poster.comp.prob_b",
            "quant.comp.prob_b", "poster.comp.prob_u"),
   pquant = 0.5, HPD = FALSE, pHPD = 0.95, pthresh = -1, unclass.na = FALSE, ...)

Arguments

object	an object of apropriate class.
prob	character string which identifies estimates of the component probabilities to be used for clustering.
pquant	when prob is either “quant.comp.prob” or “quant.comp.prob_b”, argument pquant is the probability of the quantile of the component probabilities to be used for clustering.
HPD	logical value. If TRUE then only those subjects are classified for which the lower limit of the pHPD*100% HPD credible interval of the component probability exceeds the value of ptrash.
pHPD	credible level of the HPD credible interval, see argument HPD.
pthresh	an optional threshold for the estimated component probability (when HPD is FALSE) or for the lower limit of the HPD credible interval (when HPD is TRUE) to classify a subject. No effect when pthresh is negative.
unclass.na	logical value taken into account when pthresh is positive. If unclass.na is TRUE, unclassified subjects get classification NA. If unclass.na is FALSE, unclassified subjects create a separate (last) group.
...	optional additional arguments.

Value

A data.frame with three (when HPD is FALSE) or five (when HPD is TRUE) columns.

Author(s)

Arnošt Komárek [e-mail]

See Also

NMixMCMC, GLMM_MCMC.

Examples

## TO BE ADDED.

mixAK documentation built on Sept. 25, 2023, 5:08 p.m.