[R] AIC in glm()

[yudi@stat.ucc.ie]

> Date:          Fri, 08 Dec 2000 16:53:26 +0000
> From:          Yvonnick Noel <noel at univ-lille3.fr>
> Subject:       [R] AIC in glm()
> To:            r-help at stat.math.ethz.ch

> Hello,
> 
> I just trying to play around with very simple generalized models and have 
> tried the following model :
>  
> glm(count~t1*t2,family=poisson())
> 
> on data from Jim Lindsey's book :
> 
> change
>   count t1 t2
> 1    45  1  1
> 2    13  1  2
> 3    12  2  1
> 4    54  2  2       
> 
> I get :
> 
> Call:  glm(formula = change$count ~ change$t1 * change$t2, family = poisson())
> 
> Coefficients:
>           (Intercept)             change$t12             change$t22
>                 3.807                 -1.322                 -1.242
> change$t12.change$t22
>                 2.746
> 
> Degrees of Freedom: 3 Total (i.e. Null);  0 Residual
> Null Deviance:      48.11
> Residual Deviance: 6.596e-16    AIC: 28.23
> 
> My question is :
> 
> how the AIC value is computed here? Because the deviance is null, I expected 
> an AIC of 2p = 8. But I'm probably missing something.
> 
AIC = -2 log like(at the MLE) + 2p      (Not:  deviance + 2p).

see the following:
> reg_ glm(change$count~ change$t1 * change$t2,family=poisson)
> -2*sum(log(dpois(change$count,reg$fit))) + 2*4
[1] 28.23050

(here reg$fit= change$count since it is a saturated model.)



------------------------------
Yudi Pawitan: yudi at stat.ucc.ie
Department of Statistics, UCC
Cork, Ireland
Ph : 353-1-490 2906
Fax: 353-1-427 0104
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