mixAK: NMixMCMCwrapper – R documentation

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mixAK

NMixMCMCwrapper

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 x p matrix with shifted and scaled main limits of observed intervals. z0 n x p matrix with shifted and scaled upper limits of observed intervals. censor n x 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 arnost.komarek[AT]mff.cuni.cz