[R-sig-ME] AIC and other IT indexes criteria for for backward, forward and stepwise regression
g@@d|o @end|ng |rom po@t@bgu@@c@||
Wed Dec 18 12:07:26 CET 2019
Im currently exploring on the use of AIC and other I-T indexes criteria for
backward, forward and stepwise regression.
Usually, when applying IT indexes for Multimodal Inference, we choose a set
of 'good models' depending on different criteria, but mainly, all models
with delta AIC<2, and then we averaged the estimates between the set of
models or make conclusions based on the set of models, no need to average.
However, if Im not wrong, the goal of backward etc is to get to one 'best'
final model. I understand the use of AIC in this framework but, is there
any criteria to select the best model in this case? Do I simply have to
choose the model with the lowest AIC no matter whether there is another
model whose delta is less than 2? Does it depend on a personal criteria?
For example, if my 'maximal' or saturated model has the lowest AIC and the
model dropping one variable has a delta of 0.5, which model to choose?
I was looking on the web and I have found no answer to this. So, any
literature recommendation or advice will be welcome.
Mario Garrido Escudero, PhD
Dr. Hadas Hawlena Lab
Mitrani Department of Desert Ecology
Jacob Blaustein Institutes for Desert Research
Ben-Gurion University of the Negev
Midreshet Ben-Gurion 84990 ISRAEL
gaiarrido using gmail.com; gaadio using post.bgu.ac.il
phone: (+972) 08-659-6854
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models