Obviously the easiest way to get the AIC of a linear model is by using the
lm function and then the
AIC command. However, this simple method can be very time consuming, as actually with the
lm function R computes a lot of stuff which isn't strictly needed to calculate the AIC of the model. When this is done for thousands of different models that time quickly adds up.
As I am just interested in the AIC and don't care about anything else in the model, is there any easy way of getting just that without having to calculate the full
I was thinking about using
lm.fit (which takes only 1/10 of the time of
lm) - or even better
solve(crossprod(X), t(X) %*% y) - to get the coefficients but then I am not really sure on how to get the loglikelihood function in a general way. Do you have any ideas or maybe some different approaches on how to get the AIC?
Many thanks in advance