AIC.pgam {pgam}R Documentation

AIC extraction

Description

Method for approximate Akaike Information Criterion extraction.

Usage

## S3 method for class 'pgam':
AIC(object, k = 2, ...)

Arguments

object object of class pgam holding the fitted model
k default is 2 for AIC. If k=log(n) then an approximation for BIC is obtained. Important to note that these are merely approximations.
... further arguments passed to method

Details

An approximate measure of parsimony of the Poisson-Gama Additive Models can be achieved by the expression

AIC=(D(y;hatμ)+2gle)/(n-tau)

where gle is the number of degrees of freedom of the fitted model and tau is the index of the first non-zero observation.

Value

The approximate AIC value of the fitted model.

Author(s)

Washington Leite Junger wjunger@ims.uerj.br and Antonio Ponce de Leon ponce@ims.uerj.br

References

Harvey, A. C., Fernandes, C. (1989) Time series models for count data or qualitative observations. Journal of Business and Economic Statistics, 7(4):407–417

Junger, W. L. (2004) Modelo Poisson-Gama Semi-Parametrico: Uma Abordagem de Penalizacao por Rugosidade. MSc Thesis. Rio de Janeiro, PUC-Rio, Departamento de Engenharia Eletrica

Hastie, T. J., Tibshirani, R. J.(1990) Generalized Additive Models. Chapman and Hall, London

See Also

pgam, deviance.pgam, logLik.pgam

Examples

library(pgam)
data(aihrio)
attach(aihrio)
form <- ITRESP5~f(WEEK)+HOLIDAYS+rain+PM+g(tmpmax,7)+g(wet,3)
m <- pgam(form,aihrio,omega=.8,beta=.01,maxit=1e2,eps=1e-4,optim.method="BFGS")

AIC(m)


[Package pgam version 0.4.8 Index]