jack.ppls: Jackknife estimation for PPLS-coefficients
In ppls: Penalized Partial Least Squares

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

This function computes the mean and the covariance of the regression coefficients of Penalized Partial Least Squares.

1	jack.ppls(ppls.object,ncomp,index.lambda)

`ppls.object`	an object returned by `penalized.pls.cv`
`ncomp`	integer. The number of components that are used. The default value is the cross-validation optimal number of components.
`index.lambda`	integer. The index of the penalization intensity, given by the vector `lambda` that was provided to `penalized.pls.cv`. The default value is the cross-validation optimal index.

The function needs an object returned by penalized.pls.cv. It estimates the mean and the covariance of the regression coefficient (with ncomp components and penalization intensity indexed by index.lambda). This is done via a jackknife estimate over the k cross-validation splits. We remark that this estimation step is not discussed in Kraemer, Boulesteix and Tutz (2008).

The function returns an object of class ”ppls”.

`mean.ppls`	The mean of the regression coefficients over all cross-validation splits. This is a vector of length `ncol(X)`. Note that in general, this differs from the regression coefficients computed on the whole data set, but if the number of observations is fairly large, the difference should be small.
`vcov.ppls`	The covariance matrix of the regression coefficients. This is a symmetric matrix of size `ncol(X) x ncol(X)`.
`index.lambda`	Index for the value of lambda that determines the regression coefficient.
`ncomp`	Number of components that determines the regression coefficients.
`k`	The number of cross-validation splits. These can be used to construct a t-test for the 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

penalized.pls.cv,ttest.ppls

data(cookie) # load data
X<-as.matrix(cookie[,1:700]) # extract NIR spectra
y<-as.vector(cookie[,701]) # extract one constituent

pls.object<-penalized.pls.cv(X,y,ncomp=10,kernel=TRUE) # PLS without penalization
my.jack<-jack.ppls(pls.object)