Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/calcVariationDMC.R
Calculates the posterior expectation of the variance of the individual transition probabilities as well as posterior expectation and standard deviation of the row-specific unobserved heterogeneity measure in each group to analyse how much unobserved heterogeneity is present in the various clusters (see Pamminger and Fruehwirth-Schnatter (2010) in References).
1 2 3 4 5 |
outList |
specifies a list containing the outcome (return value) of an MCMC run of |
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 |
grLabels |
A character vector giving user-specified names for the clusters/groups. |
printVarE |
If |
printUnobsHet |
If |
printUnobsHetSd |
If |
printUnobsHetAll |
If |
printAllTogether |
If |
The last maxi
MCMC draws of each thin
-th draw are taken for calculations.
A list containing:
var_e |
A 3-dim array containing the posterior expectation of the variance of the individual transition probabilities in each group. |
het |
A matrix containing the posterior expectation of the row-specific unobserved heterogeneity measure in each group. |
hetsd |
A matrix containing the posterior standard deviation of the row-specific unobserved heterogeneity measure in each group. |
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 <christoph.pamminger@gmail.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
1 | # please run the examples in dmClust, dmClustExtended
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