I have tried calculating the AIC of a linear regression in R but without using the AIC function, like this:

lm_mtcars <- lm(mpg ~ drat, mtcars)

[1] 97.98786

However, AIC gives a different value:

[1] 190.7999

Could somebody tell me what I'm doing wrong?

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    $\begingroup$ (without checking your answer yet): You're not necessarily doing anything wrong, since likelihood is actually only defined up to a multiplicative constant; two people can calculate log-likelihood and get different numbers (but differences in log-likelihood is the same). $\endgroup$ – Glen_b Feb 20 '14 at 20:26
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    $\begingroup$ Hong Oois answer is related to this question, I think. The formula that the function AIC uses is -2*as.numeric(logLik(lm_mtcars))+2*(length(lm_mtcars$coefficients)+1). $\endgroup$ – COOLSerdash Feb 20 '14 at 20:27
  • $\begingroup$ luciano: The "+1" in that formula @COOLSerdash points to arises from the variance parameter term. Note also that the function logLik says that for lm models it includes 'all constants' ... so there'll be a log(2*pi) in there somewhere $\endgroup$ – Glen_b Feb 20 '14 at 21:30
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    $\begingroup$ @Glen_b: Why say likelihood's defined only up to an multiplicative constant? After all, when comparing non-nested models from different families of distribution (e.g. with AIC, or with the Cox test), you need to remember that constant. $\endgroup$ – Scortchi Feb 20 '14 at 21:43
  • $\begingroup$ @Scortchi the definition isn't mine! You'll have to take it up with R.A.Fisher. It's been that way from the start, I think (1921). That it's still defined that way, at least in the continuous case, see here, for example, at the sentence beginning 'More precisely,'. $\endgroup$ – Glen_b Feb 20 '14 at 21:52

Note that the help on the function logLik in R says that for lm models it includes 'all constants' ... so there will be a log(2*pi) in there somewhere, as well as another constant term for the exponent in the likelihood. Also, you can't forget to count the fact that $\sigma^2$ is a parameter.

$\cal L(\hat\mu,\hat\sigma)=(\frac{1}{\sqrt{2\pi s_n^2}})^n\exp({-\frac{1}{2}\sum_i (e_i^2/s_n^2)})$

$-2\log \cal{L} = n\log(2\pi)+n\log{s_n^2}+\sum_i (e_i^2/s_n^2)$

$= n[\log(2\pi)+\log{s_n^2}+1]$

$\text{AIC} = 2p -2\log \cal{L}$

but note that for a model with 1 independent variable, p=3 (the x-coefficient, the constant and $\sigma^2$)

Which means this is how you get their answer:

  • $\begingroup$ Why in your calculation of $s^2$ are you only dividing by $n$ and not $n-p$? $\endgroup$ – Luke Thorburn Sep 28 '17 at 1:00
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    $\begingroup$ See the definition of AIC: $-2\log\mathcal{L}(\hat\theta)+2p$ where the vector of parameters, $\theta$ are evaluated at the maximum (i.e. all the elements of $\hat\theta$ are MLEs); e.g. see Wikipedia Akaike information criterion: Definition. If you're not dividing by $n$ there in the computation of $\hat{\sigma}^2$, you're not calculating the MLE of $\sigma^2$ and so not really computing AIC -- in effect you'd be adjusting twice for the effect of fitting parameters. (Yes, lots of people do it wrong) $\endgroup$ – Glen_b Sep 28 '17 at 1:01

The AIC function gives $2k -2 \log L$, where $L$ is the likelihood & $k$ is the number of estimated parameters (including the intercept, & the variance). You're using $n \log \frac{S_{\mathrm{r}}}{n} + 2(k-1)$, where $S_{\mathrm{r}}$ is the residual sum of squares, & $n$ is the sample size. These formulæ differ by an additive constant; so long as you're using the same formula & looking at differences in AIC between different models where the constants cancel, it doesn't matter.


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