[R] Interpreting the result of a model with random effects

[Mitchell Maltenfort]

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.
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
-- 
Sent from Gmail Mobile

	[[alternative HTML version deleted]]