pgam (version 0.4.12)

AIC.pgam: AIC extraction

Description

Method for approximate Akaike Information Criterion extraction.

Usage

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

Arguments

object

object of class pgam holding the fitted model

k

default is 2 for AIC. If \(k=\log\left(n\right)\) then an approximation for BIC is obtained. Important to note that these are merely approximations.

…

further arguments passed to method

Value

The approximate AIC value of the fitted model.

Details

An approximate measure of parsimony of the Poisson-Gama Additive Models can be achieved by the expression $$AIC=\left(D\left(y;\hat\mu\right)+2gle\right)/\left(n-\tau\right)$$ where \(gle\) is the number of degrees of freedom of the fitted model and \(\tau\) is the index of the first non-zero observation.

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) Semiparametric Poisson-Gamma models: a roughness penalty approach. MSc Dissertation. Rio de Janeiro, PUC-Rio, Department of Electrical Engineering.

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

See Also

pgam, deviance.pgam, logLik.pgam

Examples

# NOT RUN {
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)

# }