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.
The model parameters are selected via cross-validation. Confidence intervals ans tests for the regression coefficients can be conducted via jackknifing.
Applications include the estimation of generalized additive models and functional data. More details can be found in Kraemer, Boulesteix, and Tutz (2008).
The package also contains a data set from Near-Infrared Spectroscopy (Osborne et.al., 1984).
Nicole Kraemer <firstname.lastname@example.org>
N. Kraemer, A.-L. Boulsteix, and G. Tutz (2008). Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data. Chemometrics and Intelligent Laboratory Systems, 94, 60 - 69. http://dx.doi.org/10.1016/j.chemolab.2008.06.009
B.G. Osborne, T. Fearn, A.R. Miller, and S. Douglas (1984) Application of Near-Infrared Reflectance Spectroscopy to Compositional Analysis of Biscuits and Biscuit Dough. Journal of the Science of Food and Agriculture, 35, pp. 99 - 105.