I believe the models need to be nested wrt the model parameters in order to
perform a LRT, and if you are interested in how a single parameter changes
the fit, the models should differ in only that parameter (as much as
possible).

so to test the significance of A, you would first specify m2 (which has
parameters for both A and B), and then specify m3 (which has everything
else in m2 except A) and do anova(m2, m3).

Joel Chan, M.S.
PhD Candidate, Cognitive Psychology
Learning Research and Development Center
University of Pittsburgh
http://joelchan.me

>
> Date: Tue, 1 Jul 2014 00:21:33 -0400
> From: Mat?as Mu?oz <munozsandoval@ug.uchile.cl>
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] LRT on fixed-effects
> Message-ID:
>         <
> CAPSM5eRFDsY3PnBc1YGgcZZekErJOk8LLLzSvpbdP4S6wP0+4A@mail.gmail.com>
> Content-Type: text/plain
>
> Dear list,
>
> I have a pretty basic question about how to specify the fixed-effects
> structure to perform likelihood ratio test, I can't figure it out on my own
> and couldn't find a proper answer/example elsewhere. For example, if i have
> the following model:
>
> m1 <- lmer(y ~  A * B + (1|C), REML=F)
>
> and I'm interested in the significance of the A:B interaction term, then
> the LRT should be:
>
> m2 <- lmer(y ~ A + B +  (1|C), REML=F)
> anova(m1,m2)
>
> but if I'm also interested in testing the individual significance of A (or
> B), I'm not sure if the LRT should be performed as
>
> m3 <- lmer(y  ~  B +  (1|C), REML=F)  #to test the significance of A
>
> anova(m1,m3)
> or
> anova(m2,m3)?
>
>
> Please excuse my poor English.
> Many thanks!
> Matías
>
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