sipca: Independent Principal Component Analysis
In ajabadi/mixOmics2: Omics Data Integration Project

Description Usage Arguments Value Author(s) References See Also Examples

Performs sparse independent principal component analysis on the given data matrix to enable variable selection.

See Details of ipca.

Soft thresholding is implemented on the independent loading vectors to obtain sparse loading vectors and enable variable selection.

## S4 method for signature 'ANY'
X, ncomp  = 3, mode = csipca("deflation","parallel"),
fun = c("logcosh", "exp"), scale = FALSE, max.iter = 200,
tol = 1e-04, keepX = rep(50,ncomp), w.init = NULL)

## S4 method for signature 'MultiAssayExperiment'
sipca(X, ncomp = 2, ..., assay = NULL)

`X`	A numeric matrix (or data frame) which provides the data for the principal components analysis. It can contain missing values. Alternatively, a `MultiAssayExperiment` object.
`ncomp`	Integer, if data is complete `ncomp` decides the number of components and associated eigenvalues to display from the `pcasvd` algorithm and if the data has missing values, `ncomp` gives the number of components to keep to perform the reconstitution of the data using the NIPALS algorithm. If `NULL`, function sets `ncomp = min(nrow(X), ncol(X))`.
`...`	aguments passed to the generic.
`assay`	Name or index of an assay from `X`.

pca returns a list with class "ipca" containing the following components:

`ncomp`	the number of principal components used.
`unmixing`	the unmixing matrix of size (ncomp x ncomp)
`mixing`	the mixing matrix of size (ncomp x ncomp
`X`	the centered data matrix
`x`	the principal components (with sparse independent loadings)
`loadings`	the sparse independent loading vectors
`kurtosis`	the kurtosis measure of the independent loading vectors

Fangzhou Yao and Jeff Coquery.

Yao, F., Coquery, J. and Lê Cao, K.-A. (2011) Principal component analysis with independent loadings: a combination of PCA and ICA. (in preparation)

A. Hyvarinen and E. Oja (2000) Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430

J L Marchini, C Heaton and B D Ripley (2010). fastICA: FastICA Algorithms to perform ICA and Projection Pursuit. R package version 1.1-13.

ipca, pca, plotIndiv, plotVar and http://www.mixOmics.org for more details.

#' \dontrun{
## successful: FALSE

library(mixOmics.data)

# implement IPCA on a microarray dataset
sipca.res <- sipca(liver.toxicity$gene, ncomp = 3, mode="deflation", keepX=c(50,50,50))
sipca.res
# samples representation
plotIndiv(sipca.res, ind.names = liver.toxicity$treatment[, 4],
          group = as.numeric(as.factor(liver.toxicity$treatment[, 4])))
plotIndiv(sipca.res, cex = 1,
          col = as.numeric(as.factor(liver.toxicity$treatment[, 4])),style="3d")

# variables representation
plotVar(sipca.res, cex = 2.5)
plotVar(sipca.res, rad.in = 0.5, cex = 1,style="3d", cutoff = 0.75)
#' }