label.switching (version 1.8)

aic: Artificial Identifiability Constraints

Description

This function relabels the MCMC output by simply ordering a specific parameter. Let \(m\), \(K\) and \(J\) denote the number of simulated MCMC samples, number of mixture components and different parameter types, respectively.

Usage

aic(mcmc.pars, constraint)

Arguments

mcmc.pars

\(m\times K\times J\) array of simulated MCMC parameters.

constraint

An integer between 1 and J corresponding to the parameter that will be used to apply the Identifiabiality Constraint. In this case, the MCMC output is reordered according to the constraint $$mcmc.pars[i,1,constraint] < \ldots < mcmc.pars[i,K,constraint],$$ for all \(i=1,\ldots,m\). If constraint = "ALL", all \(J\) Identifiability Constraints are applied.

Value

permutations

an \(m\times K\) array of permutations.

See Also

permute.mcmc, label.switching

Examples

# NOT RUN {
#load a toy example: MCMC output consists of the random beta model
# applied to a normal mixture of \code{K=2} components. The number of
# observations is equal to \code{n=5}. The number of MCMC samples is
# equal to \code{m=300}. The 1000 generated MCMC samples are stored 
#to array mcmc.pars. 
data("mcmc_output")
mcmc.pars<-data_list$"mcmc.pars"

# mcmc parameters are stored to array \code{mcmc.pars}
# mcmc.pars[,,1]: simulated means of the two components
# mcmc.pars[,,2]: simulated variances of the two components
# mcmc.pars[,,3]: simulated weights of the two components
# We will apply AIC by ordering the means
# which corresponds to value \code{constraint=1}
run<-aic(mcmc = mcmc.pars,constraint=1)
# apply the permutations returned by typing:
reordered.mcmc<-permute.mcmc(mcmc.pars,run$permutations)
# reordered.mcmc[,,1]: reordered means of the two components
# reordered.mcmc[,,2]: reordered variances of the components
# reordered.mcmc[,,3]: reordered weights 
# }