jack.ppls {ppls} | R Documentation |
Jackknife Estimation for Penalized PLS Coefficients
Description
This function computes jackknife estimates (mean and covariance) of the regression coefficients obtained from a cross-validated Penalized Partial Least Squares (PPLS) model.
Usage
jack.ppls(
ppls.object,
ncomp = ppls.object$ncomp.opt,
index.lambda = ppls.object$index.lambda
)
Arguments
ppls.object |
An object returned by |
ncomp |
Integer. Number of PLS components to use. Default is |
index.lambda |
Integer. Index of the penalization parameter |
Details
The jackknife estimates are computed using the array of regression coefficients obtained in each cross-validation fold. The function returns both the mean coefficients and the associated variance-covariance matrix.
If the requested number of components ncomp
or the lambda index index.lambda
exceeds the available dimensions of the coefficients.jackknife
array, they are adjusted to their maximum allowable values, with a message.
Note: This jackknife procedure is not discussed in Kraemer et al. (2008), but it is useful for statistical inference, such as confidence intervals or hypothesis tests.
Value
An object of class "mypls"
, which is a list containing:
- coefficients
The mean regression coefficients across cross-validation splits.
- covariance
The estimated covariance matrix of the coefficients.
- k
Number of cross-validation folds used.
- ncomp
Number of components used in estimation.
- index.lambda
Index of the lambda value used.
References
N. Kraemer, A.-L. Boulesteix, and G. Tutz (2008). Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data. Chemometrics and Intelligent Laboratory Systems, 94(1), 60–69. doi:10.1016/j.chemolab.2008.06.009
See Also
penalized.pls.cv
, coef.mypls
, vcov.mypls
, ttest.ppls
Examples
data(cookie) # load example data
X <- as.matrix(cookie$NIR) # NIR spectra
y <- cookie$constituents$fat # extract one constituent
pls.object <- penalized.pls.cv(X, y, ncomp = 10, kernel = TRUE)
my.jack <- jack.ppls(pls.object)
coef(my.jack)
vcov(my.jack)