runICA: Run of fastICA and JADE algorithms

In bitona/MineICA: Analysis of an ICA decomposition obtained on genomics data

Run of fastICA and JADE algorithms

Description

This function performs ICA decomposition of a matrix using functions fastICA and JADE.

Usage

  runICA(method = c("fastICA", "JADE"), X, nbComp,
    alg.type = c("deflation", "parallel"),
    fun = c("logcosh", "exp"), maxit = 500, tol = 10^-6,
    ...)

Arguments

method	The ICA method to use, either "JADE" (the default) or "fastICA".
X	A data matrix with n rows representing observations (e.g genes) and p columns representing variables (e.g samples).
nbComp	The number of components to be extracted.
alg.type	If alg.type="parallel" the components are extracted simultaneously (the default), if alg.type="deflation" the components are extracted one at a time, see fastICA.
fun	The functional form of the G function used in the approximation to neg-entropy (see 'details' of the help of function fastICA).
maxit	The maximum number of iterations to perform.
tol	A positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged.
...	Additional parameters for fastICA and JADE

Details

See details of the functions fastICA and JADE.

Value

A list, see outputs of fastICA and JADE. This list includes at least three elements:

A

the estimated mixing matrix

S

the estimated source matrix

, itemWthe estimated unmixing matrix

Author(s)

Anne Biton

Examples

set.seed(2004);
M <- matrix(rnorm(5000*6,sd=0.3),ncol=10)
M[1:10,1:3] <- M[1:10,1:3] + 2
M[1:100,1:3] <- M[1:100,1:3] +1
resJade <- runICA(X=M, nbComp=2, method = "JADE", maxit=10000)

bitona/MineICA documentation built on April 23, 2023, 1:41 p.m.