bayesMCClust (version 1.0)

calcTransProbs: Calculates the Posterior Expectation and Standard Deviations of the Average Cluster-Specific Transition Matrices

Description

Calculates the posterior expectation and standard deviations of the average cluster-specific transition matrices and also offers some other analyses like plotting paths of MCMC draws.

Usage

calcTransProbs(outList, estGroupSize, thin = 1, M0 = outList$Mcmc$M0, 
               grLabels = paste("Group", 1:outList$Prior$H), 
               printXtable = FALSE, printSd = FALSE, 
               printTogether = TRUE, plotPaths = TRUE, 
               plotPathsForE = TRUE)

Value

A list containing:estTransProbA 3-dim array containing the posterior expectation of the average transition matrices of all clusters/groups using each thin-th draw from M0 to M.estTransProbSdA 3-dim array containing the posterior standard deviations of the average transition matrices for each cluster/group.

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, 
# dmClustExtended