slfm_list: slfm_list

In slfm: Fitting a Bayesian Sparse Latent Factor Model in Gene Expression Analysis

Description

Function to fit the Bayesian Sparse Latent Factor Model to a group of data matrices within a directory. All matrices are supposed to have values representing the gene expression observed for different genes (rows) and different samples (columns).

Usage

slfm_list(
  path = ".",
  recursive = TRUE,
  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

path	path to the directory where the target data matrices are located.
recursive	logical argument (default = TRUE) indicating whether the function should look recursively inside folders.
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 (default = 1).
degenerate	logical argument (default = FALSE) indicating whether to use the degenerate version of the mixture prior for the factor loadings.

See Also

slfm, process_matrix, plot_matrix

slfm documentation built on March 26, 2020, 7:37 p.m.