I am trying to model select manually using null hypothesis testing (for various reasons I don't want to use AIC in this case). I have used the lme4
package to construct my models and the global model looks like this (data names changed);
global<- lmer(Shannon ~
+ AN:Var1
+ AN:Var2
+ AN:Var3
+ AN:Var4
+ Var1 + Var2 + Var3
+ Var4 + Var5 + Var6 + Var7 + (1|Random),
data = data, REML=FALSE)
I want to drop a variable out in turn and compare to the global using an anova()
test but it throws up various errors, what am I doing wrong?
I've already found the top models using AIC, however some recent critisism of AIC which I won't go into here means that in this case I just want to strip it back. I tried a simple anova like this:
anova(globalsessilebase, model1)
(models structured like the original post, model 1 has var1
dropped out)
which results in this:
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model1 14 437.55 488.83 -204.78 409.55
globalsessilebase 15 438.94 493.89 -204.47 408.94 0.6101 1 0.4348
which is fine as far as I know, but some for some of the models (there are 11 when each variable is dropped out seqyentially) chisq is 0, which I don't really understand.
I also just tried drop1 and that just gives me the AIC values?
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