>>>>> "AMT" == Arnau Mir Torres <[EMAIL PROTECTED]>
>>>>> on Tue, 14 Oct 2008 17:13:01 +0200 writes:
>>>>> "AMT" == Arnau Mir Torres <[EMAIL PROTECTED]>
>>>>> on Tue, 14 Oct 2008 17:13:01 +0200 writes:
AMT> Hello.
AMT> I need to know how can R compute AIC when I study a regression model?
AMT> For example, if I use these data:
AMT> growth tannin
AMT> 1 12 0
AMT> 2 10 1
AMT> 3 8 2
AMT> 4 11 3
AMT> 5 6 4
AMT> 6 7 5
AMT> 7 2 6
AMT> 8 3 7
AMT> 9 3 8
AMT> and I do
AMT> model <- lm (growth ~ tannin)
AMT> AIC(model)
AMT> R responses:
AMT> 38.75990
AMT> I know the following formula to compute AIC:
AMT> AIC= -2*log-likelihood + 2*(p+1)
AMT> In my example, it would be:
AMT> AIC=-2*log-likelihood + 2*2
AMT> but I don't know how R computes log-likelihood:
AMT> logLik(model)
AMT> 'log Lik.' -16.37995 (df=3)
and so?
Hint: Your only problem is that your 'p' is wrongly off by one.
2nd Hint: sigma is a parameter, too
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