cv.sgpls {spls}R Documentation

Compute and plot the cross-validated error for SGPLS 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.sgpls( x, y, fold=10, K, eta, scale.x=TRUE, plot.it=TRUE,
        br=TRUE, ftype='iden' )

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.
scale.x Scale predictors by dividing each predictor variable by its sample standard deviation?
plot.it Draw the heatmap of cross-validated misclassification rates?
br Apply Firth's bias reduction procedure?
ftype Type of Firth's bias reduction procedure. Alternatives are "iden" (the approximated version) or "hat" (the original version). Default is "iden".

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.

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung, D. and Keles, S. (2009). "Sparse partial least squares classification for high dimensional data" (http://www.stat.wisc.edu/~keles/Papers/C_SPLS.pdf).

See Also

print.sgpls, predict.sgpls, and coef.sgpls.

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
## Not run: cv <- cv.sgpls( prostate$x, prostate$y, K = c(1:5), eta = seq(0.1,0.9,0.1), scale.x=FALSE, fold=5 )

(sgpls( prostate$x, prostate$y, eta=cv$eta.opt, K=cv$K.opt, scale.x=FALSE ))

[Package spls version 2.1-0 Index]