cv.splsda {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.splsda( x, y, fold=10, K, eta, kappa=0.5, classifier=c('lda','logistic'), scale.x=TRUE, plot.it=TRUE )
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 should be between 0 and 0.5. Default is 0.5. |
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? |
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.splsda
, predict.splsda
,
and coef.splsda
.
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.splsda( prostate$x, prostate$y, K = c(1:5), eta = seq(0.1,0.9,0.1), scale.x=FALSE, fold=5 ) (splsda( prostate$x, prostate$y, eta=cv$eta.opt, K=cv$K.opt, scale.x=FALSE ))