sipca: Independent Principal Component Analysis

In mixOmicsTeam/mixOmics: Omics Data Integration Project

Independent Principal Component Analysis

Description

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

Usage

sipca(
  X,
  ncomp = 3,
  mode = c("deflation", "parallel"),
  fun = c("logcosh", "exp"),
  scale = FALSE,
  max.iter = 200,
  tol = 1e-04,
  keepX = rep(50, ncomp),
  w.init = NULL
)

Arguments

X	a numeric matrix (or data frame).
ncomp	integer, number of independent component to choose. Set by default to 3.
mode	character string. What type of algorithm to use when estimating the unmixing matrix, choose one of "deflation", "parallel". Default set to deflation.
fun	the function used in approximation to neg-entropy in the FastICA algorithm. Default set to logcosh, see details of FastICA.
scale	(Default=FALSE) Logical indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is FALSE for consistency with prcomp function, but in general scaling is advisable. Alternatively, a vector of length equal the number of columns of X can be supplied. The value is passed to scale.
max.iter	integer, the maximum number of iterations.
tol	a positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged, see fastICA package.
keepX	the number of variable to keep on each dimensions.
w.init	initial un-mixing matrix (unlike fastICA, this matrix is fixed here).

Details

See Details of ipca.

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

Value

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
prop_expl_var	Proportion of the explained variance of derived components, after setting possible missing values to zero.

Author(s)

Fangzhou Yao, Jeff Coquery, Francois Bartolo, Kim-Anh Lê Cao, Al J Abadi

References

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.

See Also

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

Examples

data(liver.toxicity)

# 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])))

## Not run: 
plotIndiv(sipca.res, cex = 0.01, 
          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 = .6, style="3d")

## End(Not run)

mixOmicsTeam/mixOmics documentation built on Nov. 4, 2024, 8:56 a.m.