[R] Interpreting the result of a model with random effects
bgunter@4567 @end|ng |rom gm@||@com
Sat Jun 11 18:57:45 CEST 2022
Please note: I am not an expert.
1. If there are only 3 centers (you didn't say) , then they are not a
random selection from a larger collection of centers and a random
effects model is inappropriate anyway;
2. Otherwise, you want to estimate a centers variance component from a
sample of size 3 ??
3. You cannot -- or at least should not -- compare nested fixed
effects models (with and without 'center') with different random
effects structures. The number of df associated with random effects is
unknown, and standard (asymptotic) likelihood ratio tests are wrong.
There's a big literature on this.
So my answer is no -- the anova p-value comparison is nonsense.
Again, note my initial caveat -- perhaps it will serve as an
invitation for an expert to respond.
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Sat, Jun 11, 2022 at 8:14 AM Frank S. <f_j_rod using hotmail.com> wrote:
> Dear R users,
> I'm analyzing a particular score "y" among several individuals, each of which belongs to a center, a factor with three
> different levels (3 possible centers). I have treated the "center" as a fixed effect, and as a random term (package lme4):
> 1) model.fix <- glm(y ~ var.1 + var.2 + var.3 + var.4 + var.5 + center, family = "binomial", data = dat)
> 2) model.rand <- glmer(y ~ var.1 + var.2 + var.3 + var.4 + var.5 + (1 | center), family = "binomial", data = dat)
> The issue is that both models provide exactly the same coefficients and p-values for the 5 baseline variables, so I assumed
> that it was due to the small number of levels (in fact, too few ). However, when computing anova(model.rand, model.fix),
> the output indicates a p-value < 0.001 in favour of the "model.rand". What's happening? Should I take the random terms?
> Thanks for any help!
> Frank S.
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