slfm {slfm} | R Documentation |
This function is used to fit a Bayesian Sparse Latent Factor Model to evaluate patterns in gene expression data matrices.
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')
DOI:10.18637/jss.v090.i09
DOI:10.1007/978-3-319-12454-4_15
mat <- matrix(rnorm(2000), nrow = 20) slfm(mat, sample = 1000)