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.
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.
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.
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 lower case AICC mean in Akaike?
AICc: The information score of the model (the lower-case ‘c’ indicates that the value has been calculated from the AIC test corrected for small sample sizes). The smaller the AIC value, the better the model fit. Delta_AICc: The difference in AIC score between the best model and the model being compared.
Which is a better value for the AIC function?
The AIC function is 2K – 2 (log-likelihood). Lower AIC values indicate a better-fit model, and a model with a delta-AIC (the difference between the two AIC values being compared) of more than -2 is considered significantly better than the model it is being compared to. Is this article helpful?
How is the Akaike information criterion ( AIC ) calculated?
The Akaike information criterion is calculated from the maximum log-likelihood of the model and the number of parameters (K) used to reach that likelihood. The AIC function is 2K – 2 (log-likelihood).