[R] Interpreting the result of a model with random effects
mm@|ten @end|ng |rom gm@||@com
Sat Jun 11 17:21:38 CEST 2022
Looks like the center effect improves overall accuracy while being
independent of the other terms.
A few things to try
Compare coef(model.fix) to fixef(model.rand).
Add center as a fixed effect to model .fix
Try a conditional logit (clogit from survival)
See how consistent the coefficients are
On Sat, Jun 11, 2022 at 11:14 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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