NMixPredDA: Discriminant analysis based on MCMC output from the mixture...

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

Discriminant analysis based on MCMC output from the mixture model

Description

It performs discriminant analysis based on sampled (re-labeled) MCMC chains from the mixture model fitted with NMixMCMC function. Observations to be discriminated may be censored.

Discrimination is based on posterior predictive probabilities of belonging to (re-labeled) mixture components.

Usage

NMixPredDA(object, y0, y1, censor, inity, info)

Arguments

object	an object of class NMixMCMC
y0	vector, matrix or data frame with observations (or limits of censored-observations) to be clustered. See NMixMCMC for details. If y0 is not given then the function discriminates original observations used to generate MCMC sample stored in object.
y1	vector, matrix or data frame with upper limits of interval-censored observations (if there are any). See NMixMCMC for details.
censor	vector, matrix or data frame with censoring indicators (if there are any censored observations). See NMixMCMC for details.
inity	optional vector, matrix or data frame with initial values of censored observations (if there are any censored observations)
info	number which specifies frequency used to re-display the iteration counter during the computation.

Value

A data.frame with columns labeled prob1,..., probp giving posterior predictive probabilities of belonging to each component and a column labeled component giving the index of the component with the highest component probability.

Author(s)

Arnošt Komárek [e-mail]

See Also

NMixMCMC, NMixPlugDA.

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