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What does it mean when AIC and BIC are negative?
If your likelihood is a continuous probability function, it is not uncommon for the maximum value to be greater than 1, so if you calculate the logarithm of your value you get a positive number and (if that value is greater than k) you get a negative AIC.
What do negative AIC values mean?
Further more it is only meaningful to look at AIC when comparing models! But to answer your question, the lower the AIC the better, and a negative AIC indicates a lower degree of information loss than does a positive (this is also seen if you use the calculations I showed in the above answer, comparing AICs).
How do you interpret a negative BIC?
Generally, the aim is to minimize BIC, so if you are in a negative territory, a negative number that has the largest modulus (deepest down in the negative territory) indicates the preferred model. Hence, in your plot the best case would appear to be “2”.
Can BIC values be negative?
The BIC values are always negative, e.g. [-2000, -3000, -3300, ..] . In the documentation of the method bic() , it says “The lower the better”.
Is a higher or lower BIC better?
1 Answer. As complexity of the model increases, bic value increases and as likelihood increases, bic decreases. So, lower is better.
Why is BIC preferred over AIC?
AIC is best for prediction as it is asymptotically equivalent to cross-validation. BIC is best for explanation as it is allows consistent estimation of the underlying data generating process.
Why use AIC instead of BIC?
Which is the most negative value for AICc?
This is what occurred in your model. If you believe that comparing AICs is a good way to choose a model then it would still be the case that the (algebraically) lower AIC is preferred not the one with the lowest absolute AIC value. To reiterate you want the most negative number in your example.
What does it mean if my AIC and BIC are negative?
Under the assumption, that both models have the same log likelihood, you obviously want to choose the one with less parameters. And as you can see, it is the one with the smaller AIC (not the one with the smaller absolute value). As with likelihood, the absolute value of AIC is largely meaningless (being determined by the arbitrary constant).
What does a lower AIC mean for a model?
Lower indicates a more parsimonious model, relative to a model fit with a higher AIC. It is a relative measure of model parsimony, so it only has meaning if we compare the AIC for alternate hypotheses (= different models of the data). We can compare non-nested models. For instance, we could compare a linear to a non-linear model.
Do you care about absolute values of AIC?
The absolute values of the AIC scores do not matter. These scores can be negative or positive. In your example, the model with $\ext{AIC} = -237.847$ is preferred over the model with $\ext{AIC} = -201.928$. You should not care for the absolute values and the sign of AIC scores when comparing models.