Calculates the Entropy of a Given Classification
Calculates the entropy of a given classification based on the outcome of a specificed MCMC run of either
dmClustExtended as well
1 2 3
specifies a list containing the outcome (return value) of an MCMC run of
A matrix with dimension N x H containing the individual posterior classification probabilities
A vector of length N containing the group membership returned by
A character vector giving user-specified names for the clusters/groups.
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 <email@example.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
# please run the examples in mcClust, dmClust, mcClustExtended, # dmClustExtended, MNLAuxMix
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.