Degrees of Freedom and Statistical Inference for Partial Least Squares Regression

Documentation for package ‘plsdof’ version 0.3-2

plsdof-package	Degrees of Freedom and Statistical Inference for Partial Least Squares Regression
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	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