Calculates the entropy of a given classification based on the outcome of a specificed MCMC run of either
`mcClust`

, `mcClustExtended`

, `dmClust`

or `dmClustExtended`

as well
as `MNLAuxMix`

.

1 2 3 | ```
calcEntropy(outList, classProbs, class,
grLabels = paste("Group", 1:outList$Prior$H),
printXtable = TRUE)
``` |

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

`classProbs` |
A matrix with dimension |

`class` |
A vector of length |

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

`printXtable` |
If |

A matrix of dimension *(H+1) x 3*, where *H* is the number of clusters/groups, containing
the contribution of each cluster/group to the (total) entropy – absolute and relative to group size (number of
group members). The calculation of the entropy is based on the individual posterior classification probabilities.

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`

, `MNLAuxMix`

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

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