spls (version 2.1-2)

cv.spls: Compute and plot cross-validated mean squared prediction error for SPLS regression

Description

Draw heatmap of v-fold cross-validated mean squared prediction error and return optimal eta (thresholding parameter) and K (number of hidden components).

Usage

cv.spls( x, y, fold=10, K, eta, kappa=0.5,
        select="pls2", fit="simpls",
        scale.x=TRUE, scale.y=FALSE, plot.it=TRUE )

Arguments

x

Matrix of predictors.

y

Vector or matrix of responses.

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 when responses are multivariate. kappa shoul

select

PLS algorithm for variable selection. Alternatives are "pls2" or "simpls". Default is "pls2".

fit

PLS algorithm for model fitting. Alternatives are "kernelpls", "widekernelpls", "simpls", or "oscorespls". Default is "simpls".

scale.x

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

scale.y

Scale responses by dividing each response variable by its sample standard deviation?

plot.it

Draw heatmap of cross-validated mean squared prediction error?

Value

• Invisibly returns a list with components:
• mspemat
  Matrix of cross-validated mean squared prediction error. Rows correspond to eta and columns correspond to the number of components (K).
• eta.opt
  Optimal eta.
• K.opt
  Optimal K.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3--25.

See Also

print.spls, plot.spls, predict.spls, and coef.spls.

Examples

data(yeast)
set.seed(1)
# MSPE plot. eta is searched between 0.1 and 0.9 and
# number of hidden components is searched between 1 and 10
cv <- cv.spls( yeast$x, yeast$y, K = c(1:10), eta = seq(0.1,0.9,0.1) )
# Optimal eta and K
cv$eta.opt
cv$K.opt
(spls( yeast$x, yeast$y, eta=cv$eta.opt, K=cv$K.opt ))