slfm {slfm}R Documentation

Sparse Latent Factor Model (SLFM)

Description

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

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

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.

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

DOI:10.18637/jss.v090.i09

DOI:10.1007/978-3-319-12454-4_15

Examples

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

[Package slfm version 1.0.0 Index]