mixOmics (version 5.0-4)

plsda: Partial Least Squares Discriminant Analysis (PLS-DA).

Description

Function to perform standard Partial Least Squares regression to classify samples.

Usage

plsda(X, Y, ncomp = 2, max.iter = 500, tol = 1e-06, near.zero.var = TRUE)

Arguments

X

numeric matrix of predictors. NAs are allowed.

Y

a factor or a class vector for the discrete outcome.

ncomp

the number of components to include in the model. Default to 2.

max.iter

integer, the maximum number of iterations.

tol

a not negative real, the tolerance used in the iterative algorithm.

near.zero.var

boolean, see the internal nearZeroVar function (should be set to TRUE in particular for data with many zero values). Setting this argument to FALSE (when appropriate) will speed up the computations.

Value

• plsda returns an object of class "plsda", a list that contains the following components:
• X
  the centered and standardized original predictor matrix.
• Y
  the centered and standardized indicator response vector or matrix.
• ind.mat
  the indicator matrix.
• ncomp
  the number of components included in the model.
• mat.c
  matrix of coefficients to be used internally by predict.
• variates
  list containing the X and Y variates.
• loadings
  list containing the estimated loadings for the variates.
• names
  list containing the names to be used for individuals and variables.
• nzv
  list containing the zero- or near-zero predictors information.
• tol
  the tolerance used in the iterative algorithm, used for subsequent S3 methods
• max.iter
  the maximum number of iterations, used for subsequent S3 methods
• iter
  Number of iterations of the algorthm for each component

encoding

latin1

Details

plsda function fit PLS models with $1,...,$ncomp components to the factor or class vector Y. The appropriate indicator matrix is created.

References

Perez-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 régression PLS: theorie et pratique. Paris: Editions Technic.

See Also

splsda, summary, plotIndiv, plotVar, plot3dIndiv, plot3dVar, predict, perf and http://mixOmics.org for more details.

Examples

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

plsda.breast <- plsda(X, Y, ncomp = 2)
palette(c("red", "blue"))
col.breast <- as.numeric(as.factor(Y))
plotIndiv(plsda.breast, ind.names = TRUE, col = col.breast)
legend('bottomleft', c("After", "Before"), pch = c(16, 16), 
       col = unique(col.breast), cex = 1, pt.cex = c(1.2, 1.2), 
       title = "Treatment")
palette("default")

## Second example
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$treatment[, 4]

plsda.liver <- plsda(X, Y, ncomp = 2)
col.rat <- as.numeric(as.factor(Y))
plotIndiv(plsda.liver, col = col.rat, ind.names = Y)