compute.lower.bound: Lower bound for the Degrees of Freedom

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

Lower bound for the Degrees of Freedom

Description

This function computes the lower bound for the the Degrees of Freedom of PLS with 1 component.

Usage

compute.lower.bound(X)

Arguments

X	matrix of predictor observations.

Details

If the decay of the eigenvalues of cor(X) is not too fast, we can lower-bound the Degrees of Freedom of PLS with 1 component. Note that we implicitly assume that we use scaled predictor variables to compute the PLS solution.

Value

bound	logical. bound is TRUE if the decay of the eigenvalues is slow enough
lower.bound	if bound is TRUE, this is the lower bound, otherwise, it is set to -1

Author(s)

Nicole Kraemer

References

Kraemer, N., Sugiyama M. (2011). "The Degrees of Freedom of Partial Least Squares Regression". Journal of the American Statistical Association 106 (494) https://www.tandfonline.com/doi/abs/10.1198/jasa.2011.tm10107

See Also

pls.model

Examples

# Boston Housing data
library(MASS)
data(Boston)
X<-Boston[,-14]
my.lower<-compute.lower.bound(X)

plsdof documentation built on Dec. 1, 2022, 1:13 a.m.