cv.sgpls {spls} | R Documentation |
Draw heatmap of v-fold cross-validated misclassification rates and return optimal eta (thresholding parameter) and K (number of hidden components).
cv.sgpls( x, y, fold=10, K, eta, scale.x=TRUE, plot.it=TRUE, br=TRUE, ftype='iden' )
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" . |
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 . |
Dongjun Chung and Sunduz Keles.
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).
print.sgpls
, predict.sgpls
,
and coef.sgpls
.
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 ))