Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/calcLongRunDist.R
Calculates and plots the posterior expectation of the cluster-specific 'long-run' distribution over the categories of the outcome variable after a period of certain time units t in the various clusters starting at a specified initial state vector (corresponding to t=0). The calculation is based on the transition matrices for each cluster/group. It includes also the stationary distribution (t=Inf).
1 2 3 4 | calcLongRunDist(outList, initialStateData, class, equiDist,
thin = 1, maxi = 50, M0 = outList$Mcmc$M0,
printLongRunDist = TRUE,
grLabels = paste("Group", 1:outList$Prior$H) )
|
outList |
specifies a list containing the outcome (return value) of an MCMC run of |
initialStateData |
A vector of length N containing the initial states where to start from. |
class |
A vector of length N containing the group membership returned by |
equiDist |
A matrix of dimension (K+1) x H containing the stationary distributions (steady states) of
the Markov chains (outcome variable) in the various clusters returned by |
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 |
printLongRunDist |
If |
grLabels |
A character vector giving user-specified names for the clusters/groups. |
A barplot of the long-run distributions is drawn for each cluster/group, including also the stationary distribution (steady state).
The last maxi
MCMC draws of each thin
-th draw are taken for calculations.
A list containing the long-run distributions for each cluster/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
calcAllocations
, calcEquiDist
, mcClust
, dmClust
, mcClustExtended
,
dmClustExtended
, barplot2
1 2 | # please run the examples in mcClust, dmClust, mcClustExtended,
# dmClustExtended
|
Loading required package: gplots
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
Loading required package: xtable
Loading required package: mnormt
Loading required package: MASS
Loading required package: bayesm
Loading required package: boa
Loading required package: e1071
Loading required package: gtools
Attaching package: 'gtools'
The following object is masked from 'package:e1071':
permutations
The following object is masked from 'package:bayesm':
rdirichlet
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