ppls (version 2.0.0)

jack.ppls: 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
)

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.

Arguments

ppls.object

An object returned by penalized.pls.cv. Must contain the array coefficients.jackknife as well as fields lambda, ncomp.opt, and index.lambda.

ncomp

Integer. Number of PLS components to use. Default is ppls.object$ncomp.opt.

index.lambda

Integer. Index of the penalization parameter lambda. Default is ppls.object$index.lambda.

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.

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. tools:::Rd_expr_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)