Calculates Inefficiency Factors of the MCMC Draws Obtained for the Cluster-Specific Parameters

Description

Calculates the inefficiency factors of the MCMC draws using numEff from the R package bayesm (see References).

Usage

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calcNumEff(outList, thin = 1, printXi = TRUE, printE = TRUE, 
           printBeta = TRUE, 
           grLabels = paste("Group", 1:outList$Prior$H))

Arguments

outList

specifies a list containing the outcome (return value) of an MCMC run of mcClust, dmClust, mcClustExtended, dmClustExtended or MNLAuxMix.

thin

An integer specifying the thinning parameter (default is 1).

printXi

If TRUE (default) a LaTeX-style table containing the inefficiency factors of the cluster-specific transition matrices is generated and also printed.

printE

If TRUE (default) a LaTeX-style table containing the inefficiency factors of the cluster-specific parameter matrices is generated and also printed.

printBeta

If TRUE (default) a LaTeX-style table containing the inefficiency factors of the MNL regression coefficients is generated and also printed.

grLabels

A character vector giving user-specified names for the clusters/groups.

Value

A list containing tables of inefficiency factors:

numEffXi[h]m

Inefficiency factors of the MCMC draws obtained for each row j=1,...,K+1 of the cluster-specific transition matrices ξ_{h,j.} for each cluster/group.

numEffEhm

Inefficiency factors of the MCMC draws obtained for each row j=1,…,K+1 of the cluster-specific parameter matrices (only for DMC[Ext]) e_{h,j.} for each cluster/group.

numEffBeta

Inefficiency factors of the MCMC draws obtained for the MNL regression coefficients for each cluster.

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

Peter E. Rossi, Greg M. Allenby and Rob McCulloch, (2005), Bayesian Statistics and Marketing, Chichester: Wiley. http://www.perossi.org/home/bsm-1

See Also

numEff, mcClust, dmClust, mcClustExtended, dmClustExtended, MNLAuxMix

Examples

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

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