fabMix_parallelModels: Function for model-level parallelization
In fabMix: Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components

fabMix_parallelModels

R Documentation

Function for model-level parallelization

Description

This function runs multiple copies of the fabMix function in parallel.

Usage

fabMix_parallelModels(model, nChains, dirPriorAlphas, rawData, outDir, Kmax, mCycles, 
	burnCycles, g, h, alpha_sigma, beta_sigma, q, normalize,  
	thinning, zStart, nIterPerCycle, gibbs_z, 
	warm_up_overfitting, warm_up, overfittingInitialization, 
	progressGraphs, gwar, rmDir, parallelModels, lowerTriangular)

Arguments

`model`	Any subset of "UUU" "CUU" "UCU" "CCU" "UCC" "UUC" "CUC", "CCC" indicating the fitted models.
`nChains`	The number of parallel heated chains. When 'dirPriorAlphas' is supplied, 'nChains' can be ignored.
`dirPriorAlphas`	vector of length `nChains` in the form of an increasing sequence of positive scalars. Each entry contains the (common) prior Dirichlet parameter for the corresponding chain. Default: `dirPriorAlphas = c(1, 1 + dN*(2:nChains - 1))/Kmax`, where `dN = 1`, for `nChains > 1`. Otherwise, `dirPriorAlphas = 1/Kmax`.
`rawData`	The observed data as an `n\times p` matrix. Clustering is performed on the rows of the matrix.
`outDir`	Name of the output folder. An error is thrown if this directory already exists.
`Kmax`	Number of components in the overfitted mixture. Default: 20.
`mCycles`	Number of MCMC cycles. Each cycle consists of `nIterPerCycle` MCMC iterations. At the end of each cycle a swap of the state of two randomly chosen adjacent chains is attempted.
`burnCycles`	Number of cycles that will be discarded as burn-in period.
`g`	Prior parameter `g`. Default value: `g = 0.5`.
`h`	Prior parameter `h`. Default value: `h = 0.5`.
`alpha_sigma`	Prior parameter `\alpha`. Default value: `\alpha = 0.5`.
`beta_sigma`	Prior parameter `\beta`. Default value: `\beta = 0.5`.
`q`	A vector containing the number of factors to be fitted.
`normalize`	Should the observed data be normalized? Default value: TRUE. (Recommended)
`thinning`	Optional integer denoting the thinning of the keeped MCMC cycles.
`zStart`	Optional starting value for the allocation vector.
`nIterPerCycle`	Number of iteration per MCMC cycle. Default value: 10.
`gibbs_z`	Select the gibbs sampling scheme for updating latent allocations of mixture model. Default value: 1.
`warm_up_overfitting`	Number of iterations for the overfitting initialization scheme. Default value: 500.
`warm_up`	Number of iterations that will be used to initialize the models before starting proposing switchings. Default value: 5000.
`overfittingInitialization`	Logical value indicating whether the chains are initialized via the overfitting initialization scheme. Default: TRUE.
`progressGraphs`	Logical value indicating whether to plot successive states of the chains while the sampler runs. Default: FALSE.
`gwar`	Initialization parameter. Default: 0.05.
`rmDir`	Logical value indicating whether to delete the `outDir` directory. Default: TRUE.
`parallelModels`	Model-level parallelization: An optional integer specifying the number of cores that will be used in order to fit in parallel each member of `model`.
`lowerTriangular`	logical value indicating whether a lower triangular parameterization should be imposed on the matrix of factor loadings (if TRUE) or not. Default: TRUE.