ppls-package {ppls} | R Documentation |
Partial Least Squares in combination with a penalization term.
This package contains functions to estimate linear and nonlinear regression methods with Penalized Partial Least Squares.
Partial Leasts Squares (PLS) is a regression method that constructs latent components Xw from the data X with maximal covariance to a response y. The components are then used in a least-squares fit instead of X. For a quadratic penalty term on w, Penalized Partial Least Squares constructs latent components that maximize the penalized covariance. Applications include the estimation of generalized additive models and functional data. More details can be found in Kraemer, Boulesteix, and Tutz (2007).
Nicole Kraemer <nkraemer@cs.tu-berlin.de>
Anne-Laure Boulesteix <boulesteix@slcmsr.org>
N. Kraemer, A.-L. Boulesteix, G. Tutz (2007) "Penalized Partial Least Squares with Applications to B-Splines Transformations and Functional Data", preprint
available at http://ml.cs.tu-berlin.de/~nkraemer/publications.html