I'm using AIC for model selection, and would like to use a relative likelihood measure to quantify how many times a model with minimum AIC (AICmin) fits better than the alternative (with AICi).
For that, I'm using Burnham et al. (2011) formula, which is:
RL = exp ( 0.5 * ( AICmin - AICi ))
The expression is quite easy. However, I'm worried to miss something. In mi case, AICmin is negative (AICmin = -239.10
, AICi = 210.43
), which makes the difference term (AICmin - AICi) also negative, and thus a relative likelihood on the order of zero (RL = 2.43e-98
) and does not make sense.
In the original article I don't find any reference saying that the difference should be absolute, but if so, the ratio becomes too high (RL = 4.11+97
) to me to feel sure. Am I missing something? Thank you!
relative_likelihood <- function(x) exp(0.5 * (AICmin - x)); curve(relative_likelihood, from = 210, to = -239, n = 1001)
$\endgroup$