cobiclust: Perform a biclustering adapted to overdispersed count data.

In julieaubert/cobiclust: Biclustering via Latent Block Model 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 . The length is equal to the number of colums.
a	an numeric.
akg	a logical variable indicating whether to use a common dispersion parameter (akg = FALSE) or a dispersion parameter per cocluster (akg = TRUE).
cvg_lim	a number specifying the threshold used for convergence criterion (cvg_lim = 1e-05 by default).
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)

julieaubert/cobiclust documentation built on Sept. 7, 2020, 6:38 p.m.