# Computes Group Sizes, Group Membership and Individual Posterior Classification Probabilities

### Description

Computes (estimates) group sizes, group membership and individual posterior classification probabilities based on
the outcome of a specificed MCMC run of either `mcClust`

, `mcClustExtended`

,
`dmClust`

or `dmClustExtended`

as well as `MNLAuxMix`

.

### Usage

1 2 3 4 5 6 7 8 9 10 | ```
calcAllocationsMCC(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0, plotPathsForEta = TRUE)
calcAllocationsMCCExt(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0)
calcAllocationsDMC(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0, plotPathsForEta = TRUE)
calcAllocationsDMCExt(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0)
calcAllocationsMNL(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0)
``` |

### Arguments

`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), except for mixing proportions/weights |

`M0` |
specifies the number of the first MCMC draw after burn-in (default is |

`plotPathsForEta` |
If |

### Details

The last `maxi`

MCMC draws of each `thin`

-th draw are taken for calculations, except for mixing
proportions *η* (which are part of MCC and DMC *without* MNL extension) where *all* `thin`

-th
draws beginning at `M0`

are used.

### Value

A list containing:

`estGroupSize ` |
A vector of dimension |

`class ` |
A vector of length |

`classProbs ` |
A matrix with dimension |

### Note

The last `maxi`

MCMC draws of each `thin`

-th draw are taken for calculations, except for mixing
proportions *η* (which are part of MCC and DMC *without* MNL extension) where all `thin`

-th draws
beginning at `M0`

are used.

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 <christoph.pamminger@gmail.com>

### 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

`mcClust`

, `dmClust`

, `mcClustExtended`

, `dmClustExtended`

,
`MNLAuxMix`

### Examples

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

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