# Calculating AIC “by hand” in R

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)

nrow(mtcars)*(log((sum(lm_mtcars$residuals^2)/nrow(mtcars))))+(length(lm_mtcars$coefficients)*2)
[1] 97.98786


However, AIC gives a different value:

AIC(lm_mtcars)
[1] 190.7999


Could somebody tell me what I'm doing wrong?

• (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). – Glen_b Feb 20 '14 at 20:26
• 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$coefficients)+1). – COOLSerdash Feb 20 '14 at 20:27 • luciano: The "+1" in that formula @COOLSerdash points to arises from the variance parameter term. Note also that the function logLik says that for lm models it includes 'all constants' ... so there'll be a log(2*pi) in there somewhere – Glen_b Feb 20 '14 at 21:30 • @Glen_b: Why say likelihood's defined only up to an multiplicative constant? After all, when comparing non-nested models from different families of distribution (e.g. with AIC, or with the Cox test), you need to remember that constant. – Scortchi Feb 20 '14 at 21:43 • @Scortchi the definition isn't mine! You'll have to take it up with R.A.Fisher. It's been that way from the start, I think (1921). That it's still defined that way, at least in the continuous case, see here, for example, at the sentence beginning 'More precisely,'. – Glen_b Feb 20 '14 at 21:52 ## 2 Answers Note that the help on the function logLik in R says that for lm models it includes 'all constants' ... so there will be a log(2*pi) in there somewhere, as well as another constant term for the exponent in the likelihood. Also, you can't forget to count the fact that$\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$) Which means this is how you get their answer: nrow(mtcars)*(log(2*pi)+1+log((sum(lm_mtcars$residuals^2)/nrow(mtcars))))
+((length(lm_mtcars$coefficients)+1)*2)  • Why in your calculation of$s^2$are you only dividing by$n$and not$n-p$? – Luke Thorburn Sep 28 '17 at 1:00 • See the definition of AIC:$-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) – 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.