mcmcest-class: Finmix 'mcmcest' class

In simonsays1980/finmix: An R package for Bayesian estimation of finite mixture distributions

Description

This class stores Bayesian parameter estimates from MCMC samples and corresponding metadata. Calling mcmcestimate() returns an object of this class.

Details

Calling mcmcestimate() on an object of class mcmcoutput or mcmcoutputperm returns an object of class mcmcest that contains all Bayesian estimates together with corresponding metadata. Three Bayesian point estimates are constructed:

• BML: The Bayesian Maximum Likelihood, which is the parameter sample from MCMC sampling that maximizes the mixture likelihood.

• MAP: The Bayesian Maximum A Posterior, which is the parameter sample from MCMC sampling that maximizes the the posterior maximum likelihood.

• EAVG: The Ergodic Average over the MCMC samples without identification.

• IEAVG: The Identified Ergodic Average over the MCMC samples with identification.

Note that a model with fixed indicators (i.e. slot indicfix=TRUE) has always an identified ergodic average, because in each MCMC sample the component labels are the same and therefore identified. In contrast, a model with unknown indicators (i.e. indicfix=FALSE) suffers usually under random label switching during sampling and therefore the ergodic average over all MCMC samples is usually not identified as it averages over parameters from different components thereby pulling the component parameters together (sometimes you get the same average for all components). The ieavg is calculated for a model with unknown indicators by relabeling the component parameter samples. Re-labeling reassigns component parameters to the most likely label of the mixture in regard to the observations. As a result the mcmcest object of a model with unknown indicators will have both, an eavg and an ieavg slot containing the ergodic average over samples before and after re-labeling. The relabel slot indicates which re-labeling algorithm had been chosen.

The uncertainty of parameter estimates is measured by the standard deviation over parameters from MCMC sampling and stored in the sdpost slot. It is an estimate of the standard deviation of the true posterior parameter distribution.

The class mcmcest is a class union and includes all classes that define objects for Bayesian estimates of MCMC samples and is used to dispatch methods for mcmcest objects. For the user this detail is not important, especially as this class has no exported constructor. Objects are solely constructed internally within the function mcmcestimate().

Class Methods

Similar to the contained classes mcmcoutput this class comes along with a couple of methods that should give the user some comfort in handling the permuted sampling results. There are no setters for this class as the slots are only set internally.

Show and Summary

• show() gives a short summary of the object's slots.

• Summary() prints out a summary of estimation results.

Getters

• getDist() returns the dist slot.

• getK() returns the K slot.

• getIndicmod() returns the indicmod slot.

• getBurnin() returns the burnin slot.

• getM() returns the M slot.

• getRanperm() returns the ranperm slot.

• getRelabel() returns the relabel slot.

• getMap() returns the MAP estimates.

• getBml() returns the BML estimates.

• getEavg() returns the EAVG estimates.

• getIeavg() returns the identified EAVG estimates.

• getSdpost() returns the sdpost.

• dist A character specifying the distribution family of the mixture model used in MCMC sampling.

• K An integer specifying the number of components in the mixture model.

• indicmod A character specifying the indicator model. At this moment only a multinomial model can be chosen.

• burnin An integer specifying the number of iterations in the burn-in phase of MCMC sampling.

• M An integer specifying the number of iterations to store in MCMC sampling.

• ranperm A logical specifying, if random permutation has been used during MCMC sampling.

• relabel A character specifying the re-labeling algorithm used during parameter estimation for the identified ergodic average.

• map A named list containing the parameter estimates of the MAP. The element par is a named list and contains the component parameters and the element weight contains the weights.

• bml A named list containing the parameter estimates of the BML. The element par is a named list and contains the component parameters and the element weight contains the weights.

• eavg A named list containing the parameter estimates of the unidentified EAVG. Note that this is only the case for a model with unknown indicators.

• ieavg A named list containing the parameter estimates of the IEAVG. The element par is a named list and contains the component parameters and the element weight contains the weights.

• sdpost A named list containing the standard deviations of the parameter estimates from the posterior distributions.

simonsays1980/finmix documentation built on Dec. 23, 2021, 2:25 a.m.