R: Degrees of Freedom and Statistical Inference for Partial Least Squares Regression

# Degrees of Freedom and Statistical Inference for Partial Least Squares Regression

## Documentation for package ‘plsdof’ version 0.5-0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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
ridge.cv | Ridge Regression.
tr | Trace of a matrix
vcov.plsdof | Variance-covariance matrix
vvtz | Projectin operator