calcVariationDMC: Analyses How Much Unobserved Heterogeneity Is Present in the...

In bayesMCClust: Mixtures-of-Experts Markov Chain Clustering and Dirichlet Multinomial Clustering

Description

Calculates the posterior expectation of the variance of the individual transition probabilities as well as posterior expectation and standard deviation of the row-specific unobserved heterogeneity measure in each group to analyse how much unobserved heterogeneity is present in the various clusters (see Pamminger and Fruehwirth-Schnatter (2010) in References).

Usage

calcVariationDMC(outList, thin = 1, maxi = 50, M0 = outList$Mcmc$M0, 
                 grLabels = paste("Group", 1:outList$Prior$H), 
                 printVarE = FALSE, printUnobsHet = FALSE, 
                 printUnobsHetSd = FALSE, printUnobsHetAll = FALSE, 
                 printAllTogether = 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).
maxi	specifies the number of draws to be actually taken (after thinning) from the MCMC draws beginning from the end of the chain (default is 50).
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.
printVarE	If TRUE a LaTeX-style table of the posterior expectation of the variance of the individual transition probabilities (in percent) in each cluster/group is generated/printed.
printUnobsHet	If TRUE a LaTeX-style table of the posterior expectation of the row-specific unobserved heterogeneity measure in each group multiplied by 100 is generated/printed.
printUnobsHetSd	If TRUE a LaTeX-style table of the posterior standard deviation of the row-specific unobserved heterogeneity measure in each group multiplied by 100 is generated/printed.
printUnobsHetAll	If TRUE a LaTeX-style table of the posterior expectation and, in parenthesis, posterior standard deviation of the row-specific unobserved heterogeneity measure in each group multiplied by 100 is generated/printed.
printAllTogether	If TRUE (default) a LaTeX-style table of the posterior expectation of the variance of the individual transition probabilities (in percent) in each cluster/group as well as the posterior expectation and, in parenthesis, posterior standard deviation of the row-specific unobserved heterogeneity measure in each group multiplied by 100 is generated/printed.

Details

The last maxi MCMC draws of each thin-th draw are taken for calculations.

Value

A list containing:

var_e	A 3-dim array containing the posterior expectation of the variance of the individual transition probabilities in each group.
het	A matrix containing the posterior expectation of the row-specific unobserved heterogeneity measure in each group.
hetsd	A matrix containing the posterior standard deviation of the row-specific unobserved heterogeneity measure in each group.

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 <[e-mail]>

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

# please run the examples in dmClust, dmClustExtended

bayesMCClust documentation built on May 29, 2017, 3:31 p.m.