Calculates (And Plots) the Stationary Distribution (Steady State)

Share:

Description

Calculates (and plots) the posterior expectations of the cluster-specific stationary distributions (also equilibrium distributions or steady states) of the Markov chains (outcome variable) based on the transition matrices for each cluster/group.

Usage

1
2
3
calcEquiDist(outList, thin = 1, maxi = 50, M0 = outList$Mcmc$M0, 
             grLabels = paste("Group", 1:outList$Prior$H), 
             printEquiDist = TRUE, plotEquiDist = TRUE)

Arguments

outList

specifies a list containing the outcome (return value) of an MCMC run of mcClust, dmClust, mcClustExtended or dmClustExtended.

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).

M0

specifies the number of the first MCMC draw after burn-in (default is outList$Mcmc$M0).

grLabels

A character vector giving user-specified names for the clusters/groups.

printEquiDist

If TRUE (default) a LaTeX-style table containing the stationary distributions is generated.

plotEquiDist

If TRUE (default) a barplot of the stationary distributions is drawn.

Details

The last maxi MCMC draws of each thin-th draw are taken for calculations.

Value

A matrix of dimension (K+1) x H containing the stationary distributions (steady states) of the Markov chains (outcome variable) based on the transition matrices in the various clusters/groups. Note, H is the number of clusters/groups and K+1 the number of states of the categorical outcome variable.

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 <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, barplot2

Examples

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

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.