Hi all,
I know that normally when you fit two models identical except for one variable, like for instance:
S(envcov1)
S(envcov1+envcov2)
and you find a slightly lower AIC for the second model but a very similar Deviance you can suspect that the envcov2 is a pretending variable.
In these cases it is recommended to have a look at the beta value of the suspected pretending variable and see if the CIs are significant or not (including zero or not).
OK, now I have a case that is quite different with respect to the above scenario but I am still in doubt about how to go on, should I treat it as a pretending variable issue and remove that model from the results or not?
I have two models:
S(envcov1) Deviance:16144.1 QAICc:16269.6
S(envcov1+envcov2) Deviance 16138.8 QAICc:16266.45
The difference in the number of parameters is of course just one, but the differences in both Deviances and QAICc values are not that trivial at all.
Anyway, when I see the beta value of the envcov2 I find this:
Intercept: value(-1.36) CI-(-1.72) CI+(-0.99)
envcov1: value(0.52) CI-(0.27) CI+(0.77)
envcov2: value(-0.05) CI-(-0.36) CI+(0.26)
Any opinion on this?