PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

ppls: penalized partial least squares
Nicole Krämer and Anne-Laure Boulesteix
(2007) The Comprehensive R Archive Network.

Abstract

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.

EPrint Type:Other
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3058
Deposited By:Nicole Krämer
Deposited On:07 February 2008