plotTypicalMembers: Plots Time Series of 'Typical' Group Members

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

Description

Plots time series of the most 'typical' group members showing the highest classification probabilities.

Usage

plotTypicalMembers(outList, myObsList, classProbs, noTypMemb = 7, 
                   moreTypMemb = c(10, 25, 50, 100, 200, 500, 1000), 
                   grLabels = paste("Group", 1:outList$Prior$H))

Arguments

outList	specifies a list containing the outcome (return value) of an MCMC run of mcClust, dmClust, mcClustExtended or dmClustExtended.
myObsList	A list containing N numeric vectors (of integers with possibly variable lengths) corresponding to the individual time series.
classProbs	A matrix with dimension N x H containing the individual posterior classification probabilities returned by calcAllocations.
noTypMemb	An integer indicating the number of most typical group members to be drawn from each cluster/group.
moreTypMemb	A vector with length noTypMemb containing the positions (ranks) in the individual posterior classification probability ranking of further (typical) group members.
grLabels	A character vector giving user-specified names for the clusters/groups.

Value

A list containing:

typicalMemb	The index numbers of the individuals being the first noTypMemb most typical group members according to their positions (ranks) in the individual posterior classification probability ranking.
typicalMemb2	The index numbers of the individuals being the moreTypMemb-th most typical group members. according to their positions (ranks) in the individual posterior classification probability ranking.

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

Examples

# please run the examples in mcClust, dmClust, mcClustExtended 
# and/or dmClustExtended

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