All functions

benchmark.pls()

Comparison of model selection criteria for Partial Least Squares Regression.

benchmark.regression()

Comparison of Partial Least Squares Regression, Principal Components Regression and Ridge Regression.

coef(<plsdof>)

Regression coefficients

compute.lower.bound()

Lower bound for the Degrees of Freedom

dA()

Derivative of normalization function

dnormalize()

Derivative of normalization function

dvvtz()

First derivative of the projection operator

first.local.minimum()

Index of the first local minimum.

information.criteria()

Information criteria

kernel.pls.fit()

Kernel Partial Least Squares Fit

krylov()

Krylov sequence

linear.pls.fit()

Linear Partial Least Squares Fit

normalize()

Normalization of vectors

pcr()

Principal Components Regression

pcr.cv()

Model selection for Princinpal Components regression based on cross-validation

pls.cv()

Model selection for Partial Least Squares based on cross-validation

pls.dof()

Computation of the Degrees of Freedom

pls.ic()

Model selection for Partial Least Squares based on information criteria

pls.model()

Partial Least Squares

plsdof-package

Degrees of Freedom and Statistical Inference for Partial Least Squares Regression

ridge.cv()

Ridge Regression.

tr()

Trace of a matrix

vcov(<plsdof>)

Variance-covariance matrix

vvtz()

Projectin operator