slfm (version 0.2.1)

slfm: Sparse Latent Factor Model

Description

This function is used to fit a Bayesian sparse latent factor model.

Usage

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)

Arguments

x

matrix with the pre-processed data

a

prior shape parameter for Gamma distribution

b

prior scale parameter for Gamma distribution

gamma_a

prior parameter for Beta distribution

gamma_b

prior parameter for Beta distribution

omega_0

prior variance of the spike component

omega_1

prior variance of the slab component

sample

sample size after burn-in

burnin

burn-in size

lag

lag for MCMC

degenerate

use the degenerate version of mixture

Value

• 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`)

References

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.

Examples

mat <- matrix(rnorm(2000), nrow = 20)
slfm(mat, sample = 1000)