dataBased: Data-based labelling
In label.switching: Relabelling MCMC Outputs of Mixture Models

Description Usage Arguments Value Author(s) References See Also Examples

This function reorders the MCMC output according the data-based relabelling algorithm of Rodriguez and Walker (2014). The idea is to define a loss function which resembles a k-means type diveging measure of cluster centers. After the cluster centers have been estimated, the algorithm finds the optimal permutations that switch every simulated MCMC sample to them.

1	dataBased(x, K, z)

`x`	n-dimensional data vector/array.
`K`	the number of mixture components.
`z`	m\times n integer array of the latent allocation vectors generated from an MCMC algorithm.

permutations

m\times K dimensional array of permutations

Panagiotis Papastamoulis

Rodriguez C.E. and Walker S. (2014). Label Switching in Bayesian Mixture Models: Deterministic relabeling strategies. Journal of Computational and Graphical Statistics. 23:1, 25-45

permute.mcmc, label.switching

#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")
z<-data_list$"z"
K<-data_list$"K"
x<-data_list$"x"
mcmc.pars<-data_list$"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
# Apply dataBased relabelling
run<-dataBased(x = x, K = K, z = z)
# 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