spls (version 2.1-2)

cv.splsda: Compute and plot cross-validated error for SPLSDA classification

Description

Draw heatmap of v-fold cross-validated misclassification rates and return optimal eta (thresholding parameter) and K (number of hidden components).

Usage

cv.splsda( x, y, fold=10, K, eta, kappa=0.5,
        classifier=c('lda','logistic'), scale.x=TRUE, plot.it=TRUE, n.core=8 )

Arguments

x

Matrix of predictors.

y

Vector of class indices.

fold

Number of cross-validation folds. Default is 10-folds.

K

Number of hidden components.

eta

Thresholding parameter. eta should be between 0 and 1.

kappa

Parameter to control the effect of the concavity of the objective function and the closeness of original and surrogate direction vectors. kappa is relevant only for multicategory classification. kappa shou

classifier

Classifier used in the second step of SPLSDA. Alternatives are "logistic" or "lda". Default is "lda".

scale.x

Scale predictors by dividing each predictor variable by its sample standard deviation?

plot.it

Draw the heatmap of the cross-validated misclassification rates?

n.core

Number of CPUs to be used when parallel computing is utilized.

Value

• Invisibly returns a list with components:
• err.mat
  Matrix of cross-validated misclassification rates. Rows correspond to eta and columns correspond to number of components (K).
• eta.opt
  Optimal eta.
• K.opt
  Optimal K.

Details

Parallel computing can be utilized for faster computation if multicore package is installed. Users can change the number of CPUs to be used by changing the argument n.core.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

See Also

print.splsda, predict.splsda, and coef.splsda.

Examples

data(prostate)
set.seed(1)
# misclassification rate plot. eta is searched between 0.1 and 0.9 and
# number of hidden components is searched between 1 and 5
cv <- cv.splsda( prostate$x, prostate$y, K = c(1:5), eta = seq(0.1,0.9,0.1), scale.x=FALSE, fold=5 )
cv <- list(); cv$eta.opt <- 0.8; cv$K.opt <- 3;
(splsda( prostate$x, prostate$y, eta=cv$eta.opt, K=cv$K.opt, scale.x=FALSE ))