penalized.pls.cv: Cross-validation for Penalized PLS
In ppls: Penalized Partial Least Squares

Description Usage Arguments Value Author(s) References See Also Examples

Computes the cross-validated error of penalized PLS for different values of lambda and components, and returns the parameter values and coefficients for the optimal model.

1	penalized.pls.cv(X, y, P, lambda, ncomp, k, kernel,scale)

`X`	matrix of input data
`y`	vector of responses
`P`	Penalty matrix. For the default value `P=NULL`, no penalty term is used, i.e. ordinary PLS is computed.
`lambda`	vector of candidate parameters lambda for the amount of penalization. Default value is 1
`ncomp`	Number of penalized PLS components to be computed. Default value is min(nrow(X)-1,ncol(X))
`k`	the number of splits in `k`-fold cross-validation. Default value is `k`=5.
`kernel`	Logical value. If `kernel=TRUE`, the kernelized version of penalized PLS is computed. Default value is `kernel=FALSE`
`scale`	logical value. If scale=TRUE, the X variables are standardized to have unit variance. Default value is FALSE

`error.cv`	matrix of cross-validated errors. The rows correspond to the values of lambda, the columns correspond to the number of components.
`lambda`	vector of candidate parameters lambda for the amount of penalization
`lambda.opt`	Optimal value of lambda
`index.lambda`	Index for the optimal value of lambda
`ncomp.opt`	Optimal number of penalized PLS components
`min.ppls`	Cross-validated error for the optimal penalized PLS solution
`intercept`	Intercept for the optimal model, computed on the whole data set
`coefficients`	Regression coefficients for the optimal model, computed on the whole data set
`coefficients.jackknife`	array of regression coefficients for each cross-validation run and each parameter setting. The dimension is `ncol(X) x ncomp x length(lambda) x k`. This result can be used to estimate the variance of the regression coefficients.

Nicole Kraemer

N. Kraemer, A.-L. Boulsteix, and G. Tutz (2008). Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data. Chemometrics and Intelligent Laboratory Systems 94, 60 - 69. http://dx.doi.org/10.1016/j.chemolab.2008.06.009

ppls.splines.cv,penalized.pls,new.penalized.pls,jack.ppls

# the penalty term in this example does not make much
# sense
X<-matrix(rnorm(20*100),ncol=20)
y<-rnorm(rnorm(100))
P<-Penalty.matrix(m=20)
pen.pls<-penalized.pls.cv(X,y,lambda=c(0,1,10),P=P,ncomp=10,kernel=FALSE)