Description Usage Arguments Value See Also Examples
Perform a biclustering adapted to overdispersed count data.
1 2 3 4 5 6 7 8 9 10 11 |
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). |
An object of class cobiclustering
cobiclustering
for the cobiclustering class.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | 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)
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