calcParMatDMC: Calculates the Posterior Expectation of the Cluster-Specific...
In bayesMCClust: Mixtures-of-Experts Markov Chain Clustering and Dirichlet Multinomial Clustering

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

Calculates the posterior expectation of the cluster-specific parameter matrices e_h (only for DMC[Ext]).

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calcParMatDMC(outList, thin = 1, M0 = outList$Mcmc$M0, 
              grLabels = paste("Group", 1:outList$Prior$H), 
              printPar = TRUE)

`outList`	specifies a list containing the outcome (return value) of an MCMC run of `dmClust` or `dmClustExtended`.
`thin`	An integer specifying the thinning parameter (default is 1).
`M0`	specifies the number of the first MCMC draw after burn-in (default is `outList$Mcmc$M0`).
`grLabels`	A character vector giving user-specified names for the clusters/groups.
`printPar`	If `TRUE` (default) a LaTeX-style table containing the posterior expectation of the cluster-specific parameter matrices e_h is also printed.

A 3-dim array containing the posterior expectation of the cluster-specific parameter matrices e_h.

Note, that in contrast to the literature (see References), the numbering (labelling) of the states of the categorical outcome variable (time series) in this package is sometimes 0,...,K (instead of 1,...,K), however, there are K+1 categories (states)!

Christoph Pamminger <christoph.pamminger@gmail.com>

Sylvia Fruehwirth-Schnatter, Christoph Pamminger, Andrea Weber and Rudolf Winter-Ebmer, (2011), "Labor market entry and earnings dynamics: Bayesian inference using mixtures-of-experts Markov chain clustering". Journal of Applied Econometrics. DOI: 10.1002/jae.1249 http://onlinelibrary.wiley.com/doi/10.1002/jae.1249/abstract

Christoph Pamminger and Sylvia Fruehwirth-Schnatter, (2010), "Model-based Clustering of Categorical Time Series". Bayesian Analysis, Vol. 5, No. 2, pp. 345-368. DOI: 10.1214/10-BA606 http://ba.stat.cmu.edu/journal/2010/vol05/issue02/pamminger.pdf

dmClust, dmClustExtended

1 2	# please run the examples in mcClust, dmClust, mcClustExtended, # dmClustExtended

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