# tools:::Rd_package_title("fabMix") tools:::Rd_package_description("fabMix") The main fuction of the package is `fabMix`. package ## Author(s) Panagiotis Papastamoulis Maintainer: tools:::Rd_package_maintainer("fabMix") ## References Fokoue, E. and Titterington, D.M. (2003). Mixtures of Factor Analysers: Bayesian Estimation and Inference by Stochastic Simulation. Machine Learing, 50(1): 73-94. McNicholas, P.D. and Murphy, T.B. Statistics and Computing (2008) 18: 285. https://doi.org/10.1007/s11222-008-9056-0. Papastamoulis P. and Iliopoulos G. (2010). An artificial allocations based solution to the label switching problem in Bayesian analysis of mixtures of distributions. Journal of Computational and Graphical Statistics, 19: 313-331. Rousseau, J. and Mengersen, K. (2011). Asymptotic behaviour of the posterior distribution in overfitted mixture models. Journal of the Royal Statistical Society, Series B (methodological), 73(5): 689-710. van Havre, Z., White, N., Rousseau, J. and Mengersen, K. (2015). Overfitting Bayesian Mixture Models with an Unknown Number of Components. PLOS ONE, 10(7): 1-27. Papastamoulis, P. (2016). `label.switching`: An R Package for Dealing with the Label Switching Problem in MCMC Outputs. Journal of Statistical Software, 69(1), 1-24. Papastamoulis, P. (2018). Overfitting Bayesian mixtures of factor analyzers with an unknown number of components. Computational Statistics and Data Analysis, 124: 220-234. DOI: 10.1016/j.csda.2018.03.007. Papastamoulis, P (2019). Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components. Statistics and Computing, doi: 10.1007/s11222-019-09891-z. ## See Also `fabMix`, `plot.fabMix.object` ## Examples ```r # TOY EXAMPLE (very small numbers... only for CRAN check purposes) ################################################################# # (a) using 2 cores in parallel, each one running 2 heated chains. ################################################################# library('fabMix') n = 8 # sample size p = 5 # number of variables q = 2 # number of factors K = 2 # true number of clusters sINV_diag = 1/((1:p)) # diagonal of inverse variance of errors set.seed(100) syntheticDataset <- simData(sameLambda=TRUE,K.true = K, n = n, q = q, p = p, sINV_values = sINV_diag) colnames(syntheticDataset$data) <- paste0("x_",1:p) # Run `fabMix` for a _small_ number of iterations # considering only `UUU` (maximal model), # using the default prior parallel heating parameters `dirPriorAlphas`. # NOTE: `dirPriorAlphas` may require some tuning in general. qRange <- 2 # values for the number of factors (only the true number # is considered here) Kmax <- 2 # number of components for the overfitted mixture model nChains <- 2 # number of parallel heated chains set.seed(1) fm <- fabMix( model = c("UUU"), nChains = nChains, rawData = syntheticDataset$data, outDir = "toyExample", Kmax = Kmax, mCycles = 4, burnCycles = 1, q = qRange, g = 0.5, h = 0.5, alpha_sigma = 0.5, beta_sigma = 0.5, warm_up_overfitting = 2, warm_up = 5) # WARNING: the following parameters: # Kmax, nChains, mCycles, burnCycles, warm_up_overfitting, warm_up # should take (much) _larger_ values. E.g. a typical implementation consists of: # Kmax = 20, nChains >= 3, mCycles = 1100, burnCycles = 100, # warm_up_overfitting = 500, warm_up = 5000. # Now print a run summary and produce some plots. print(fm) # you may also plot, summary the output. ################################################################# # (b) using 12 cores_____________________________________________ #_______4 models with 3 heated chains running in parallel________ #_______considering all 8 model parameterizations________________ ################################################################# ## Not run: library('fabMix') set.seed(99) n = 100 # sample size p = 30 # number of variables q = 2 # number of factors K = 5 # number of clusters sINV_diag = rep(1/100,p) # diagonal of inverse variance of errors syntheticDataset <- simData(sameLambda=FALSE,K.true = K, n = n, q = q, p = p, sINV_values = sINV_diag) colnames(syntheticDataset$data) <- paste0("x_",1:p) qRange <- 1:3 # range of values for the number of factors Kmax <- 20 # number of components for the overfitted mixture model nChains <- 3 # number of parallel heated chains # the next command takes ~ 1 hour in a Linux workstation with 12 threads. fm <- fabMix( parallelModels = 4, nChains = nChains, model = c("UUU","CUU","UCU","CCU","UCC","UUC","CUC","CCC"), rawData = syntheticDataset$data, outDir = "toyExample_b", Kmax = Kmax, mCycles = 600, burnCycles = 100, q = qRange, g = 0.5, h = 0.5, alpha_sigma = 0.5, beta_sigma = 0.5, warm_up_overfitting = 500, warm_up = 5000) print(fm) plot(fm, what = "BIC") plot(fm, what = "classification_pairs") ## End(Not run) ```

fabMix-package