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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