Contents
What does infinite AIC mean?
If AIC is infinite you have L << 1. In case of a least squares this can only happen if variance is zero. Probably you have one or a set of covariates that together are perfectly collinear with the outcome In other words, there is nothing left in the residuals.
How does step AIC work?
“stepAIC” does not necessarily mean to improve the model performance, however, it is used to simplify the model without impacting much on the performance. So AIC quantifies the amount of information loss due to this simplification. Also in case of multiple models, the one which has lower AIC value is preferred.
What does negative infinity in AIC infer?
The negative infinity in AIC infers very overfitted model in the model selection. It is fortunate that your stepAIC was stopped. A working demo below. Validate the quality of your original data. In model selection such as Forward Stepwise, we have a special condition called breakdown point which is needed to ensure the quality of the model.
Why does stepaic start at the most complex model?
As a metric, AIC only makes sense relative to other values; its absolute value has no meaning. So in the procedure in your code stepAIC () is starting at the most complex model (because direction = “backward” ), and sequentially removing terms in an effort to lower the AIC.
What is the AIC of the improved model?
The “improved” model includes only hp and wt as predictor variables and has an AIC of 63.840. We now repeat the same process as the one described in the paragraph above – we start out with the “improved” model, eliminate each of the remaining predictors hp and wt in turns from the model and monitor how that impacts the AIC.
What happens to the AIC when you eliminate a predictor?
If the elimination of a predictor improves the “maximal” model, the AIC should become smaller. It turns out that when eliminate the predictor disp from the “maximal” model, we achieve a smaller AIC (i.e., AIC = 63.840), than the one corresponding to the “maximal” model (i.e., AIC = 65.831).