SM.MAP.MixReparametrized {Ultimixt} | R Documentation |
summary of the output produced by K.MixReparametrized
Description
Label switching in a simulated Markov chain produced by K.MixReparametrized is removed by the technique of Marin et al.
(2004). Namely, component labels are reorded by the shortest Euclidian distance between a posterior sample and the maximum a
posteriori (MAP) estimate. Let be the
-th vector of computed component means, standard deviations
and weights. The MAP estimate is derived from the MCMC sequence and denoted by
. For a permutation
the labelling of
is reordered by
where .
Angular parameters and
s are
derived from
. There exists an unique solution in
while there are multiple solutions in
due to the symmetry of
and
. The output of
only includes angles on
.
The label of components of (before the above transform) is defined by
The number of label switching occurrences is defined by the number of changes in .
Usage
SM.MAP.MixReparametrized(estimate, xobs, alpha0, alpha)
Arguments
estimate |
Output of K.MixReparametrized |
xobs |
Data set |
alpha0 |
Hyperparameter of Dirichlet prior distribution of the mixture model weights |
alpha |
Hyperparameter of beta prior distribution of the radial coordinate |
Details
Details.
Value
MU |
Matrix of MCMC samples of the component means of the mixture model |
SIGMA |
Matrix of MCMC samples of the component standard deviations of the mixture model |
P |
Matrix of MCMC samples of the component weights of the mixture model |
Ang_SIGMA |
Matrix of computed |
Ang_MU |
Matrix of computed |
Global_Mean |
Mean, median and |
Global_Std |
Mean, median and |
Phi |
Mean, median and |
component_mu |
Mean, median and |
component_sigma |
Mean, median and |
component_p |
Mean, median and |
l_stay |
Number of MCMC iterations between changes in labelling |
n_switch |
Number of label switching occurrences |
Note
Note.
Author(s)
Kate Lee
References
Marin, J.-M., Mengersen, K. and Robert, C. P. (2004) Bayesian Modelling and Inference on Mixtures of Distributions, Handbook of Statistics, Elsevier, Volume 25, Pages 459–507.
See Also
Examples
#data(faithful)
#xobs=faithful[,1]
#estimate=K.MixReparametrized(xobs,k=2,alpha0=0.5,alpha=0.5,Nsim=1e4)
#result=SM.MAP.MixReparametrized(estimate,xobs,alpha0=0.5,alpha=0.5)