Penalized Partial Least Squares
Extract Regression Coefficients from a mypls Object
Plot Penalized PLS Components for Spline-Transformed Data
Jackknife Estimation for Penalized PLS Coefficients
Normalize a Numeric Vector to Unit Length
Predict New Data Using a Penalized PLS Model
Penalty Matrix for Higher Order Differences
Cross-Validation for Penalized PLS with Spline-Transformed Predictors
Simulate Data for Penalized Partial Least Squares (PPLS)
t-Test for Penalized PLS Regression Coefficients
Variance-Covariance Matrix for Penalized PLS Coefficients
Nonlinear Transformation via B-Splines
Linear and nonlinear regression methods based on Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing. The method is described and applied to simulated and experimental data in Kraemer et al. (2008) <doi:10.1016/j.chemolab.2008.06.009>.