This function computes the lower bound for the the Degrees of Freedom of PLS with 1 component.
compute.lower.bound(X)
X | matrix of predictor observations. |
---|
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
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
Nicole Kraemer