plsRglm (version 0.7.4)

plsR.dof: Computation of the Degrees of Freedom

Description

This function computes the Degrees of Freedom using the Krylov representation of PLS and other quantities that are used to get information criteria values. For the time present, it only works with complete datasets.

Usage

plsR.dof(modplsR, naive = FALSE)

Arguments

modplsR

A plsR model i.e. an object returned by one of the functions plsR, plsRmodel.default, plsRmodel.formula, PLS_lm or PLS_lm_formula.

naive

A boolean.

Value

• DoF
  Degrees of Freedom
• sigmahat
  Estimates of dispersion
• Yhat
  Predicted values
• yhat
  Square Euclidean norms of the predicted values
• RSS
  Residual Sums of Squares

Details

If naive=FALSE returns values for estimated degrees of freedom and error dispersion. If naive=TRUE returns returns values for naive degrees of freedom and error dispersion. The original code from Nicole Kraemer and Mikio L. Braun was unable to handle models with only one component.

References

N. Kraemer, M. Sugiyama, The Degrees of Freedom of Partial Least Squares Regression. Preprint (2010). http://arxiv.org/abs/1002.4112. N. Kraemer, M. Sugiyama, M.L. Braun, Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), (2009) 272-279.

See Also

aic.dof and infcrit.dof for computing information criteria directly from a previously fitted plsR model.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsR(yCornell,XCornell,4)
plsR.dof(modpls) 
plsR.dof(modpls,naive=TRUE)