Non-statisticians tend to use step-wise regressions which is strongly argued by statisticians. This is something that I don't understand (unfortunately, I am not a statistician), but I just obey them. "Ok this is not a good way to do your modelling".
Here is (was) my model:
b <- lmer(metric1~A+B+C+D+E+F+G+H+I+J+K+(1|X/Y) + (1|Z), data = dataset)
drop1 (b, test="Chisq")
(Just a small note: Watch out for the random effects in my model; random effects are Year, Month, Sampling.location; one of my variables is 1/0: I allready log-transformed my variables)
I am trying to find an exploratory model (with
drop1 to reach final model) and evaluating it with my biological knowledge to see if the dependent ("metric" in this case) seems to be responding variables. I will repeat this process with 100 metrics just to evaulate which metrics seems to be responding environmental variables.
I was in the search for an acceptable model instead of stepwise according to the suggestions of statistics gurus.
However, there are lots of alternatives. I read alot, but still feel myself lost. Some say Lasso, some say elastic modelling, some say ridge regression... Which one fits for my purpose?
edit: meanwhile I have some tiny progress on
glmulti. Is that a proper way of doing the thing that I aim?
Any advise for a better alternative and an easy model or a help page for dummies, or examples (that could be better) would be much appreciated.