infcrit.dof: Information criteria

In fbertran/plsRglm: Partial Least Squares Regression for Generalized Linear Models

Information criteria

Description

This function computes information criteria for existing plsR model using Degrees of Freedom estimation.

Usage

infcrit.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.

Details

If naive=FALSE returns AIC, BIC and gmdl values for estimated and naive degrees of freedom. If naive=TRUE returns NULL.

Value

matrix

AIC, BIC and gmdl values or NULL.

Author(s)

Frédéric Bertrand
[e-mail]
https://fbertran.github.io/homepage/

References

M. Hansen, B. Yu. (2001). Model Selection and Minimum Descripion Length Principle, Journal of the American Statistical Association, 96, 746-774.
N. Kraemer, M. Sugiyama. (2011). The Degrees of Freedom of Partial Least Squares Regression. Journal of the American Statistical Association, 106(494), 697-705.
N. Kraemer, M. Sugiyama, M.L. Braun. (2009). Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 272-279.

See Also

plsR.dof for degrees of freedom computation 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)
infcrit.dof(modpls)

fbertran/plsRglm documentation built on March 23, 2023, 2:14 a.m.