Calculates the Posterior Expectation of the Cluster-Specific Parameter Matrices (only for DMC[Ext])

Description

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

Usage

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

Arguments

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.

Value

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

Note

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)!

Author(s)

Christoph Pamminger <christoph.pamminger@gmail.com>

References

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

See Also

dmClust, dmClustExtended

Examples

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

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