This function runs multiple copies of the fabMix function in parallel.
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)An object of class fabMix.object (see the fabMix function).
Any subset of "UUU" "CUU" "UCU" "CCU" "UCC" "UUC" "CUC", "CCC" indicating the fitted models.
The number of parallel heated chains. When `dirPriorAlphas` is supplied, `nChains` can be ignored.
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.
The observed data as an \(n\times p\) matrix. Clustering is performed on the rows of the matrix.
Name of the output folder. An error is thrown if this directory already exists.
Number of components in the overfitted mixture. Default: 20.
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.
Number of cycles that will be discarded as burn-in period.
Prior parameter \(g\). Default value: \(g = 0.5\).
Prior parameter \(h\). Default value: \(h = 0.5\).
Prior parameter \(\alpha\). Default value: \(\alpha = 0.5\).
Prior parameter \(\beta\). Default value: \(\beta = 0.5\).
A vector containing the number of factors to be fitted.
Should the observed data be normalized? Default value: TRUE. (Recommended)
Optional integer denoting the thinning of the keeped MCMC cycles.
Optional starting value for the allocation vector.
Number of iteration per MCMC cycle. Default value: 10.
Select the gibbs sampling scheme for updating latent allocations of mixture model. Default value: 1.
Number of iterations for the overfitting initialization scheme. Default value: 500.
Number of iterations that will be used to initialize the models before starting proposing switchings. Default value: 5000.
Logical value indicating whether the chains are initialized via the overfitting initialization scheme. Default: TRUE.
Logical value indicating whether to plot successive states of the chains while the sampler runs. Default: FALSE.
Initialization parameter. Default: 0.05.
Logical value indicating whether to delete the outDir directory. Default: TRUE.
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.
logical value indicating whether a lower triangular parameterization should be imposed on the matrix of factor loadings (if TRUE) or not. Default: TRUE.
Panagiotis Papastamoulis