calcEntropy: Calculates the Entropy of a Given Classification

In bayesMCClust: Mixtures-of-Experts Markov Chain Clustering and Dirichlet Multinomial Clustering

Description

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.

Usage

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

Arguments

outList	specifies a list containing the outcome (return value) of an MCMC run of mcClust, dmClust, mcClustExtended, dmClustExtended or MNLAuxMix.
classProbs	A matrix with dimension N x H containing the individual posterior classification probabilities returned by calcAllocations.
class	A vector of length N containing the group membership returned by calcAllocations.
grLabels	A character vector giving user-specified names for the clusters/groups.
printXtable	If TRUE (default) a LaTeX-style table of the entropy is generated.

Value

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

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 <[e-mail]>

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

calcAllocations, mcClust, dmClust, mcClustExtended, dmClustExtended, MNLAuxMix

Examples

# please run the examples in mcClust, dmClust, mcClustExtended, 
# dmClustExtended, MNLAuxMix

bayesMCClust documentation built on May 29, 2017, 3:31 p.m.