sPLSda function

Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

Function to perform sparse Partial Least Squares to classify samples (supervised analysis) and select variables. latin1

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.

Returns

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(s)

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))