Description Usage Arguments Details Value Note Author(s) References See Also Examples
Computes (estimates) group sizes, group membership and individual posterior classification probabilities based on
the outcome of a specificed MCMC run of either mcClust
, mcClustExtended
,
dmClust
or dmClustExtended
as well as MNLAuxMix
.
1 2 3 4 5 6 7 8 9 10 | calcAllocationsMCC(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0, plotPathsForEta = TRUE)
calcAllocationsMCCExt(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0)
calcAllocationsDMC(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0, plotPathsForEta = TRUE)
calcAllocationsDMCExt(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0)
calcAllocationsMNL(outList, thin = 1, maxi = 50,
M0 = outList$Mcmc$M0)
|
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), except for mixing proportions/weights η where all |
M0 |
specifies the number of the first MCMC draw after burn-in (default is |
plotPathsForEta |
If |
The last maxi
MCMC draws of each thin
-th draw are taken for calculations, except for mixing
proportions η (which are part of MCC and DMC without MNL extension) where all thin
-th
draws beginning at M0
are used.
A list containing:
estGroupSize |
A vector of dimension H containing the posterior mean of group sizes. For MCC and DMC
without MNL extension |
class |
A vector of length N containing the group membership, which is determined for each individual according to the maximum individual posterior classification probability. |
classProbs |
A matrix with dimension N x H containing the individual posterior classification
probabilities which are calculated using the last |
The last maxi
MCMC draws of each thin
-th draw are taken for calculations, except for mixing
proportions η (which are part of MCC and DMC without MNL extension) where all thin
-th draws
beginning at M0
are used.
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
mcClust
, dmClust
, mcClustExtended
, dmClustExtended
,
MNLAuxMix
1 2 | # please run the examples in mcClust, dmClust, mcClustExtended,
# dmClustExtended, MNLAuxMix
|
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