[R] AIC questions

[Wensui Liu]

joe,
some procs in SAs calculates log likelihood differently than what it
is supposed to be. try using proc nlmixed and specifying the LL
explicitly.
in your case, I has stronger faith in R result instead of SAS result.

On 9/26/07, Joe Yarmus <joseph.yarmus at oracle.com> wrote:
> In accordance with Venables and Ripley, SAS documentation and other
> sources AIC with sigma^2 unknown is calculated as:
> AIC = -2LL + 2* #parameters =  n log(RSS/n) + 2p
> For the fitness data:
> (http://support.sas.com/ctx/samples/index.jsp?sid=927), SAS gets an AIC
> of 64.534 with model oxygen = runtime. (SAS STAT User's Guide. Chapter
> 61. pp 3956, the REG Procedure). This value of AIC accords with p = 2.
>
> When I run the same problem in R ver 2.5.1, I get
>
>  > rt.glm =glm(oxy ~ runtime, data=fitness)
>  > rt.glm
> Call:  glm(formula = oxy ~ runtime, data = fitness)
>
> Coefficients:
> (Intercept)      runtime
>      82.422       -3.311
>
> Degrees of Freedom: 30 Total (i.e. Null);  29 Residual
> Null Deviance:        851.4
> Residual Deviance: 218.5     AIC: 154.5
>
> I get very close to what R gets if the constant term is included in
> -2LL, (31*Log(2*pi)+n-1), divide RSS by n-1 and the number of parameters
> is 3 (the predictor, the intercept and the error term)
>  > 31 * (log(2*pi)+log(sum(rt.glm$res^2)/30)) + 30 + 2 * 3
> [1] 154.5248
>  > AIC(rt.glm)
> [1] 154.5083
>
> 3 questions:
> 1) Why the discrepancy between SAS and R?
> 2) Why the slight difference between my calculation in R and R's AIC?
> 3) How should AIC be computed if row weights are used in the linear model?
>
> Thanks!
>
>     -joe yarmus
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>


-- 
===============================
"I am dying with the help of too many
physicians." - Alexander the Great, on his deathbed
===============================
WenSui Liu
(http://spaces.msn.com/statcompute/blog)