slfm: Sparse Latent Factor Model (SLFM)
In jdanielnd/slfm: Tools for Fitting Sparse Latent Factor Model

Description Usage Arguments Value References Examples

This function is used to fit a Bayesian Sparse Latent Factor Model to evaluate patterns in gene expression data matrices.

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slfm(x, a = 2.1, b = 1.1, gamma_a = 1, gamma_b = 1,
  omega_0 = 0.01, omega_1 = 10, sample = 1000, burnin = round(0.25
  * sample), lag = 1, degenerate = FALSE)

`x`	matrix with the pre-processed data.
`a`	positive shape parameter of the Inverse Gamma prior distribution (default = 2.1).
`b`	positive scale parameter of the Inverse Gamma prior distribution (default = 1.1).
`gamma_a`	positive 1st shape parameter of the Beta prior distribution (default = 1).
`gamma_b`	positive 2nd shape parameter of the Beta prior distribution (default = 1).
`omega_0`	prior variance of the spike mixture component (default = 0.01).
`omega_1`	prior variance of the slab mixture component (default = 10).
`sample`	sample size to be considered for inference after the burn in period (default = 1000).
`burnin`	size of the burn in period in the MCMC algorithm (default = sample/4).
`lag`	lag to build the chains based on spaced draws from the Gibbs sampler (defaul = 1).
`degenerate`	logical argument (default = FALSE) indicating whether to use the degenerate version of the mixture prior for the factor loadings.

x: data matrix.

q_star: matrix of MCMC chains for q_star parameter.

alpha: summary table of MCMC chains for alpha parameter.

lambda: summary table of MCMC chains for lambda parameter.

sigma: summary table of MCMC chains for sigma parameter.

classification: classification of each alpha ('present', 'marginal', 'absent')

1. Duarte, J. D. N. and Mayrink, V. D. (2015). Factor analysis with mixture modeling to evaluate coherent patterns in microarray data. In Interdisciplinary Bayesian Statistics, volume 118 of Springer Proceedings in Mathematics & Statistics, pages 185-195. Springer International Publishing.