AIC.pgam | R Documentation |
Method for approximate Akaike Information Criterion extraction.
## S3 method for class 'pgam' AIC(object, k = 2, ...)
object |
object of class |
k |
default is 2 for AIC. If k=\log≤ft(n\right) then an approximation for BIC is obtained. Important to note that these are merely approximations. |
... |
further arguments passed to method |
An approximate measure of parsimony of the Poisson-Gama Additive Models can be achieved by the expression
AIC=≤ft(D≤ft(y;\hatμ\right)+2gle\right)/≤ft(n-τ\right)
where gle is the number of degrees of freedom of the fitted model and τ is the index of the first non-zero observation.
The approximate AIC value of the fitted model.
Washington Leite Junger wjunger@ims.uerj.br and Antonio Ponce de Leon ponce@ims.uerj.br
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
pgam
, deviance.pgam
, logLik.pgam
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)
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