cobiclust: Perform a biclustering adapted to overdispersed count data.

In cobiclust: Biclustering via Latent Block Model Adapted to Overdispersed Count Data

Perform a biclustering adapted to overdispersed count data.

Description

Perform a biclustering adapted to overdispersed count data.

Usage

cobiclust(
  x,
  K = 2,
  G = 3,
  nu_j = NULL,
  a = NULL,
  akg = FALSE,
  cvg_lim = 1e-05,
  nbiter = 5000,
  tol = 1e-04
)

Arguments

x	the input matrix of observed data.
K	an integer specifying the number of groups in rows.
G	an integer specifying the number of groups in columns.
nu_j	a vector of numeric, corresponding of a column (sampling effort) effect.
a	a numeric dispersion parameter (parameter of the gamma distribution).
akg	a logical variable indicating whether to use a common dispersion parameter (akg = FALSE) or not.
cvg_lim	a number specifying the threshold used for convergence criterion.
nbiter	the maximal number of iterations for the global loop of variational EM algorithm (nbiter = 5000 by default).
tol	the level of relative iteration convergence tolerance (tol = 1e-04 by default).

Value

An object of class cobiclustering

See Also

cobiclustering for the cobiclustering class.

Examples

npc <- c(50, 40) # nodes per class
KG <- c(2, 3) # classes
nm <- npc * KG # nodes
Z <- diag(KG[1]) %x% matrix(1, npc[1], 1)
W <- diag(KG[2]) %x% matrix(1, npc[2], 1)
L <- 70*matrix(runif(KG[1] * KG[2]), KG[1], KG[2])
M_in_expectation <- Z %*% L %*% t(W)
size <- 50
M <- matrix(
  rnbinom(
    n = length(as.vector(M_in_expectation)),
    mu = as.vector(M_in_expectation), size = size
  ),
  nm[1], nm[2]
)
rownames(M) <- paste('OTU', 1:nrow(M), sep = '_')
colnames(M) <- paste('S', 1:ncol(M), sep = '_')
res <- cobiclust(M, K = 2, G = 3, nu_j = rep(1, 120), a = 1 / size, cvg_lim = 1e-5)

cobiclust documentation built on May 29, 2024, 6:23 a.m.