hi: the definition of AIC can vary a lot from paper to paper and textbook
to textbook because some people keep the multiplicative constants and
other's don't. all that matters when using AIC is COMPARISON. the value
itself means nothing. So, you'll be fine no matter what
you use as long as you're consistent.
On Mon, Feb 13, 2012 at 1:22 PM, david hamer <j.david.ha...@gmail.com>wrote:
> Using the Cement hardening data in Anderson (2008) Model Based Inference in
> the Life Sciences. A Primer on Evidence, and working with the best model
> which is
> lm ( y ~ x1 + x2, data = cement )
> the AIC value from R is
>
> model <- lm ( formula = y ~ x1 + x2 , data =
> cement )
> AIC ( model )
>
> 64.312
>
> which can be converted to AICc by adding the bias correction factor
> 2*K*(K+1)/(n-K-1) to give the AICc value of
> 69.312
> (addition of 5, where n=13 and K=4).
>
> This same value, 69.31, can be obtained using R package
> AICcmodavg
>
> library ( AICcmodavg )
> data (cement)
> cement
> Cand.models <- list( )
> Cand.models[[1]] <- lm ( y ~ x1 + x2, data = cement )
> Cand.models[[2]] <- lm ( y ~ x3 + x4, data = cement )
> Cand.models[[3]] <- lm ( y ~ x1 + x2 + x1 * x2, data =
> cement )
> Cand.models[[4]] <- lm ( y ~ x3 + x4 + x3 * x4, data =
> cement )
> ## vector of model names
> Modnames <- paste("MODEL", 1:4, sep=" ")
> ## AICc
> aictab ( cand.set = Cand.models, modnames = Modnames )
>
> However, the AICc value reported by Anderson (2008) is
> 32.41.
> The AICc value obtained using RSS value (i.e., calculating AICc "manually"
> from the output of linear regression) is
> 32.41.
>
> Thanks for any help.
> David
> New R user, minimal familiarity with statistics.
>
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>
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