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This function computes the lower bound for the the Degrees of Freedom of PLS with 1 component.
compute.lower.bound(X)logical. bound is TRUE if the decay of the
eigenvalues is slow enough
if bound is TRUE, this is the lower bound, otherwise, it is set to -1
matrix of predictor observations.
Nicole Kraemer
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.
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
pls.model
# Boston Housing data
library(MASS)
data(Boston)
X<-Boston[,-14]
my.lower<-compute.lower.bound(X)
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