bayesMCClust (version 1.0)

calcEntropy: Calculates the Entropy of a Given Classification

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)

Value

A matrix of dimension $(H+1) \times 3$, 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.

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