mixOmics (version 2.8-1)

splsda: Sparse Partial Least Squares Discriminate Analysis (sPLS-DA)

Description

Function to perform sparse Partial Least Squares to classify samples. The sPLS-DA approach embeds variable selection for this purpose.

Usage

splsda(X, Y, ncomp = 2, keepX = c(rep(ncol(X), ncomp)), max.iter = 500, scaleY =TRUE, mode = 'regression', tol = 1e-06)

Arguments

X

numeric matrix of predictors. NAs are allowed.

Y

a factor or or a class vector for the discrete outcome.

ncomp

the number of components to include in the model (see Details).

keepX

numeric vector of length ncomp, the number of variables to keep in $X$-loadings. By default all variables are kept in the model.

max.iter

integer, the maximum number of iterations.

scaleY

logical, indicates if Y should be scaled or not.

mode

indicates the mode of analysis. It can be either 'regression', 'classic' or 'invariant'

tol

a positive real, the tolerance used in the iterative algorithm.

Value

• splsda returns an object of class "spls", a list that contains the following components:
• X
  the centered and standardized original predictor matrix.
• Y
  the indicator matrix.
• ncomp
  the number of components included in the model.
• keepX
  number of X variables kept in the model on each component.
• mat.c
  matrix of coefficients to be used internally by predict.
• variates
  list containing the variates.
• loadings
  list containing the estimated loadings for the X and Y variates.
• names
  list containing the names to be used for individuals and variables.

encoding

latin1

Details

splsda function fit sPLS models with $1, \ldots ,$ncomp components to the indicator matrix Y. If a factor is supplied, the appropriate indicator matrix is created.

References

L� Cao, K.-A., Martin, P.G.P., Robert-Grani�, C. and Besse, P. (2009). Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinformatics 10:34. L� Cao, K.-A., Rossouw, D., Robert-Grani�, C. and Besse, P. (2008). A sparse PLS for variable selection when integrating Omics data. Statistical Applications in Genetics and Molecular Biology 7, article 35. Shen, H. and Huang, J. Z. (2008). Sparse principal component analysis via regularized low rank matrix approximation. Journal of Multivariate Analysis 99, 1015-1034. P�rez-Enciso, M. and Tenenhaus, M. (2003). Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Human Genetics 112, 581-592. Nguyen, D. V. and Rocke, D. M. (2002). Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18, 39-50. Tenenhaus, M. (1998). La r�gression PLS: th�orie et pratique. Paris: Editions Technic. Wold H. (1966). Estimation of principal components and related models by iterative least squares. In: Krishnaiah, P. R. (editors), Multivariate Analysis. Academic Press, N.Y., 391-420.

See Also

spls, summary, plotIndiv, plotVar, plot3dIndiv, plot3dVar, cim, network.

Examples

# First example
data(breast.tumors)
X <- breast.tumors$gene.exp
Y <- as.factor(breast.tumors$sample$treatment)

res <- splsda(X, Y, ncomp = 2, keepX = c(25, 25))
palette(c("red", "blue"))
plotIndiv(res, ind.names = TRUE, col = as.numeric(Y))
legend(-0.35, -0.19, c("After", "Before"), pch = c(16, 16), 
       col = c("red", "blue"), cex = 1, pt.cex = c(1.2, 1.2), 
       title = "Treatment")
palette("default")


# Second example
data(liver.toxicity)
X = as.matrix(liver.toxicity$gene)
Y = as.factor(liver.toxicity$treatment[,4])
ncomp = 2
keepX = 20
splsda.liver = splsda(X, Y, ncomp = ncomp, keepX = c(rep(keepX, ncomp)),  mode = 'regression')
col = as.numeric(Y)
plotIndiv(splsda.liver, col = col)