penalized.pls.cv {ppls} | R Documentation |
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 |
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.opt |
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 |
Nicole Kr"amer
N. Kr"amer, 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.
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)