Calculates the 'segmentation power' and optionally the 'sharpness' of the specified classification. The
'segmentation power' corresponds to the *maximum* individual posterior classification probability. The closer
the *maximum* individual posterior classification probability is to 1, the higher is the segmentation power for
individual *i*. Note that one minus these numbers corresponds to the *misclassification risk* in each
group; hence the closer to one, the smaller is the misclassification risk.

The 'sharpness' on the other hand considers the difference between highest (maximum) and second highest individual posterior classification probabilities, which gives some hints about the 'sharpness' of the classification.

1 2 3 4 |

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

`classProbs` |
A matrix with dimension |

`class` |
A vector of length |

`printXtable` |
If |

`calcSharp` |
If |

`printSharpXtable` |
If |

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

Reported are summary statistics including the quartiles and the median of the distributions of the segmentation power and the 'sharpness' for all individuals within a certain cluster/group as well as for all individuals.

A list containing:

`segPowTab ` |
A matrix containing the segmentation power: reported are summary statistics of the distribution of the maximum individual posterior classification probabilities for all individuals within a certain cluster as well as for all individuals. |

`sharpTab ` |
A matrix containing the 'sharpness': reported are summary statistics of the difference between highest and second highest individual posterior classification probabilities within groups and overall. |

`maxProbs ` |
A vector containing the |

`sharp ` |
A vector containing the differences of the individual maximum and the second highest 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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