NMixMCMCwrapper: Wrapper to the NMixMCMC main simulation.

In mixAK: Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering

Wrapper to the NMixMCMC main simulation.

Description

This is wrapper to the NMixMCMC main simulation which allows vectorized evaluation and possibly parallel computation.

THIS FUNCTION IS NOT TO BE CALLED BY ORDINARY USERS.

Usage

NMixMCMCwrapper(chain = 1,
                scale, prior, inits, Cpar, RJMCMC, CRJMCMC,
                actionAll, nMCMC, keep.chains, PED,
                dens.zero, lx_w)

Arguments

chain	identification of the chain sampled in a particular call of this function, usually number like 1, 2, ...
Cpar	a list with the following components z0 n\times p matrix with shifted and scaled main limits of observed intervals. z0 n\times p matrix with shifted and scaled upper limits of observed intervals. censor n\times p matrix with censoring indicators. p dimension of the response. n number of observations. Cinteger a numeric vector with integer prior parameters. Cdouble a numeric vector with double precission prior parameters. lx_w a character vector with levels of an optional factor covariate on the mixture weights.
scale	a list specifying how to scale the data before running MCMC. See argument scale in NMixMCMC
prior	a list specifying prior hyperparameters. See argument prior in NMixMCMC.
inits	a list of length at least chain. Its chain-th component is used. Each component of the list should have the structure of init argument of function NMixMCMC.
RJMCMC	a list specifying parameters for RJ-MCMC. See argument RJMCMC in NMixMCMC
CRJMCMC	a numeric vector with parameters for RJ-MCMC.
actionAll	argument for underlying C++ function.
nMCMC	vector giving the length of MCMC etc.
keep.chains	logical. If FALSE, only summary statistics are returned in the resulting object. This might be useful in the model searching step to save some memory.
PED	a logical value which indicates whether the penalized expected deviance (see Plummer, 2008 for more details) will be computed (which requires two parallel chains). Even if keep.chains is FALSE, it is necessary to keep (for a while) at least some chains to compute PED.
dens.zero	small number (1e-300) to determine whether the contribution to the deviance (-\log density) is equal to infinity. Such values are trimmed when computing expected deviance.

Value

A list having almost the same components as object returned by NMixMCMC function.

Author(s)

Arnošt Komárek [e-mail]

See Also

NMixMCMC.

mixAK documentation built on Sept. 25, 2023, 5:08 p.m.