Artificial Identifiability Constraints
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.
aic(mcmc.pars, constraint)
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] < … < mcmc.pars[i,K,constraint], for all i=1,…,m. If |
permutations |
an m\times K array of permutations. |
Panagiotis Papastamoulis
#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 weightsPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.