Hi,
In order to find the best models I use AIC, more specifically I calculate
Akaike weights then Evidence Ratio (ER) and consider that models with a ER <
2 are equally likely.
But the same problem remain each time I do that. I selected the best models
from a set of them, but I don't know if those models are efficient to
predict (or at least represent) my data.
I can have selected the best element(s) of the list of the worst models.
Do you find it is correct to calculate R2 or pseudo-R2 for the best "set of
models" in order to have an idea of the representativeness of those models
and use this value to select the more efficient model ?
I would be glad to hear your opinions about this !
Thanks,
Arnaud
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