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 
ncomp 
integer. The number of components that are used. The default value is the crossvalidation optimal number of components. 
index.lambda 
integer. The index of the penalization intensity, given by the vector 
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
crossvalidation 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 crossvalidation splits. This is a vector of length 
vcov.ppls 
The covariance matrix of the regression coefficients. This is a symmetric matrix of size 
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 crossvalidation splits. These can be used to construct a ttest for the coefficients. 
Nicole Kraemer
N. Kraemer, A.L. Boulsteix, and G. Tutz (2008). Penalized Partial Least Squares with Applications to BSpline 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
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