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How do I compare two AIC?
To compare models using AIC, you need to calculate the AIC of each model. If a model is more than 2 AIC units lower than another, then it is considered significantly better than that model. You can easily calculate AIC by hand if you have the log-likelihood of your model, but calculating log-likelihood is complicated!
Can Akaike information Criteria be negative?
Yes. It’s valid to compare AIC values regardless they are positive or negative. That’s because AIC is defined be a linear function (-2) of log-likelihood. If the likelihood is large, your AIC will be likely negative but it says nothing about the model itself.
What should AIC be?
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Which is better a positive or negative AIC score?
The absolute values of the AIC scores do not matter. These scores can be negative or positive. In your example, the model with AIC = − 237.847 is preferred over the model with AIC = − 201.928. You should not care for the absolute values and the sign of AIC scores when comparing models.
Is it possible to find a negative value for AICc?
To reiterate you want the most negative number in your example. Generally, it is assumed that AIC (and so AICc) is defined up to adding a constant, so the fact if it is negative or positive is not meaningful at all. So the answer is yes, it is valid. Even if the constant is included, the AIC (AICc) can be negative.
Which is the best model with the lowest AIC?
For model comparison, the model with the lowest AIC score is preferred. The absolute values of the AIC scores do not matter. These scores can be negative or positive. In your example, the model with AIC = − 237.847 is preferred over the model with AIC = − 201.928.
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.