Calculates (and plots) the posterior expectations of the cluster-specific stationary distributions (also equilibrium distributions or steady states) of the Markov chains (outcome variable) based on the transition matrices for each cluster/group.

1 2 3 | ```
calcEquiDist(outList, thin = 1, maxi = 50, M0 = outList$Mcmc$M0,
grLabels = paste("Group", 1:outList$Prior$H),
printEquiDist = TRUE, plotEquiDist = TRUE)
``` |

`outList` |
specifies a list containing the outcome (return value) of an MCMC run of |

`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 |

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

`printEquiDist` |
If |

`plotEquiDist` |
If |

The last `maxi`

MCMC draws of each `thin`

-th draw are taken for calculations.

A matrix of dimension *(K+1) x H* containing the stationary distributions (steady states) of
the Markov chains (outcome variable) based on the transition matrices in the various clusters/groups. Note, *H*
is the number of clusters/groups and *K+1* the number of states of the categorical outcome variable.

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

`mcClust`

, `dmClust`

, `mcClustExtended`

, `dmClustExtended`

,
`barplot2`

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

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