penalized.pls.cv {ppls} | R Documentation |
This function 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.
penalized.pls.cv(X, y, P, lambda, ncomp, k, kernel,scale)
X |
A n x p matrix of input data, with observations corresponding to the rows and variables to the columns. |
y |
A numeric n-vector of responses. |
P |
The p x p penalty matrix. For the default value P=NULL , no penalty term is used,
i.e. ordinary PLS is computed. |
lambda |
A numeric vector giving the (positive) candidate values for the parameter lambda determining the amount of penalization. Default value is 1. |
ncomp |
The maximum 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 |
If kernel=TRUE , the kernelized version of
penalized PLS is computed. Default value is kernel=FALSE . |
scale |
If scale=TRUE , the X variables are
standardized to have unit variance. Default value is scale=FALSE . |
lambda.opt |
The optimal value of the parameter lambda. |
ncomp.opt |
The optimal number of penalized PLS components. |
min.ppls |
The cross-validated error for the optimal penalized PLS solution. |
intercept |
The intercept for the optimal model, computed on the whole data set. |
coefficients |
The regression coefficients for the optimal model, computed on the whole data set. |
Nicole Kraemer
N. Kraemer, A.-L. Boulesteix, G. Tutz (2007) "Penalized Partial Least Squares with Applications to B-Splines Transformations and Functional Data", preprint
available at http://ml.cs.tu-berlin.de/~nkraemer/publications.html
ppls.splines.cv
,penalized.pls
,new.penalized.pls
# 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)