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Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

sPLSda.Rd

Function to perform sparse Partial Least Squares to classify samples (supervised analysis) and select variables.

Usage

sPLSda(X, Y, ncomp = 2, keepX = rep(ncol(X), ncomp),
       max.iter = 500, tol = 1e-06)

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 (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.

tol

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

Details

sPLSda function fit sPLS models with \(1, \ldots ,\)ncomp components to the factor or class vector Y. The appropriate indicator (dummy) matrix is created.

Value

sPLSda returns an object of class "sPLSda", 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.

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.

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

References

On sPLS-DA: Le Cao, K.-A., Boitard, S. and Besse, P. (2011). Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 12:253.

Author

Benoit Liquet and Pierre Lafaye de Micheaux.

See also

sPLS, summary, plotIndiv, plotVar, cim, network, predict, perf and http://www.mixOmics.org for more details.

Examples

### Examples from mixOmics packages  


data(liver.toxicity)
X <- as.matrix(liver.toxicity$gene)
# Y will be transformed as a factor in the function,
# but we set it as a factor to set up the colors.
Y <- as.factor(liver.toxicity$treatment[, 4])

model <- sPLSda(X, Y, ncomp = 2, keepX = c(20, 20))