NMixMCMC
or GLMM_MCMC function and internally re-labels the mixture
components using selected re-labeling algorithm. It also computes
posterior summary statistics for mixture means, weights, variances
which correspond to newly labeled MCMC sample. Further, posterior
component probabilities (poster.comp.prob1 and
poster.comp.prob2 components of the object object) are
updated according to the newly labeled MCMC sample.This function only works for models with a fixed number of mixture components.
NMixRelabel(object, type=c("mean", "weight", "stephens"), par, ...)## S3 method for class 'default':
NMixRelabel(object, type=c("mean", "weight", "stephens"), par, ...)
## S3 method for class 'NMixMCMC':
NMixRelabel(object, type=c("mean", "weight","stephens"), par,
prob=c(0.025, 0.5, 0.975), keep.comp.prob=FALSE, info, ...)
## S3 method for class 'GLMM_MCMC':
NMixRelabel(object, type=c("mean", "weight", "stephens"), par,
prob=c(0.025, 0.5, 0.975), keep.comp.prob=FALSE, info, ...)
[object Object],[object Object],[object Object]
TRUE, posterior sample of
component allocation probabilities (for each subject) is kept in the resulting object.object argument in
which the following components are updated according to new labeling
of the mixture components. Jasra, A., Holmes, C. C., and Stephens, D. A. (2005).
Markov chain Monte Carlo methods and the label switching problem in
Bayesian mixture modeling.
Statistical Science, 20, 50-67.
Stephens, M. (1997).
Bayesian methods for mixtures of normal distributions. DPhil Thesis.
Oxford: University of Oxford.
(Available from:
NMixMCMC, GLMM_MCMC.## See also additional material available in
## YOUR_R_DIR/library/mixAK/doc/
## or YOUR_R_DIR/site-library/mixAK/doc/
## - files PBCseq.pdf,
## PBCseq.R
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