calcTransProbs: Calculates the Posterior Expectation and Standard Deviations...
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 and standard deviations of the average cluster-specific transition matrices and also offers some other analyses like plotting paths of MCMC draws.

calcTransProbs(outList, estGroupSize, thin = 1, M0 = outList$Mcmc$M0, 
               grLabels = paste("Group", 1:outList$Prior$H), 
               printXtable = FALSE, printSd = FALSE, 
               printTogether = TRUE, plotPaths = TRUE, 
               plotPathsForE = TRUE)

`outList`	specifies a list containing the outcome (return value) of an MCMC run of `mcClust`, `dmClust`, `mcClustExtended` or `dmClustExtended`.
`estGroupSize`	A vector of dimension H containing the (estimated) group sizes returned by `calcAllocations`.
`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.
`printXtable`	If `TRUE` a LaTeX-style table containing the posterior expectation of the average cluster-specific transition matrices of each cluster/group is generated/printed.
`printSd`	If `TRUE` a LaTeX-style table containing the posterior standard deviations (multiplied by 100) of the average cluster-specific transition matrices of each cluster/group is generated/printed.
`printTogether`	If `TRUE` (default) a LaTeX-style table containing the posterior expectation and standard deviations (multiplied by 100) of the average cluster-specific transition matrices of each cluster/group is generated/printed.
`plotPaths`	If `TRUE` (default) the paths of the MCMC draws of the transition probabilities ξ_{h,j,k} are drawn for each cluster/group.
`plotPathsForE`	If `TRUE` (default) the paths of the MCMC draws of the transition parameters e_{h,j,k} are drawn for each cluster/group (only DMC[Ext]).

A list containing:

`estTransProb`	A 3-dim array containing the posterior expectation of the average transition matrices of all clusters/groups using each `thin`-th draw from `M0` to `M`.
`estTransProbSd`	A 3-dim array containing the posterior standard deviations of the average transition matrices for each cluster/group.

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

calcAllocations, mcClust, dmClust, mcClustExtended, dmClustExtended

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

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