Thanks for your answers. I appreciate your help. I tried the glmmML.
However, it seems glmmML does not allow for a quasibinomial fit as I did
with the models I used. I have large overdispersion which I account for
using a quasibinomial with scaling parameter. Further, I have 360
observations - is that considered large enough for asymptotics?
The capacity covariate ranges from 2 to 5 in steps of 1. I repeated the
analysis subtracting 2 (because then the "0" capacity makes more sense and
is of intrinsic interest) and get the "same" results. The group and
group*capacity interaction make sense as I want to investigate a level and a
slope difference for the groups. However, I am worried about the correlation
of fixed effects. LMER gives me the following correlation matrix for the
fixed effects:
(Intr) I(c-2) group2 group3 I(-2):2
I(capcty-2) -0.143
group2 -0.707 0.101
group3 -0.705 0.101 0.499
I(c-2):grp2 0.104 -0.730 -0.135 -0.074
I(c-2):grp3 0.104 -0.725 -0.073 -0.129 0.529
I will try to leave out the capacity effect altogether and just model a
group and a group slope effect. Does that make sense?
Thanks,
Daniel
-------------------------
cuncta stricte discussurus
-------------------------
-----Ursprüngliche Nachricht-----
Von: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Im
Auftrag von Ben Bolker
Gesendet: Monday, July 07, 2008 1:41 PM
An: [EMAIL PROTECTED]
Betreff: Re: [R] GLM, LMER, GEE interpretation
Daniel Malter <daniel <at> umd.edu> writes:
>
> Hi, my dependent variable is a proportion ("prob.bind"), and the
> independent variables are factors for group membership ("group") and a
> covariate ("capacity"). I am interested in the effects of group,
> capacity, and their interaction. Each subject is observed on all (4)
> levels of capacity (I use capacity as a covariate because the effect
> of this variable is normatively linear). I fit three models, but I am
> observing differences between the three.
>
> The first model is a quasibinomial without any subject effects using glm.
> The second is a random-effects model using lmer. The third model is a
> generalized estimating equation using gee from the gee package in
> which I cluster for the subject using an unstructured correlation
> matrix. The results of the first and the third model almost coincide,
> but the second, using lmer, shows an insginficant coefficient where I
> would expect a significant one. The other 2 models show the
> coefficient significant. I do not really have experience with gee.
> Therefore I apologize in advance for my ignorant question whether one
> of lmer and gee is preferable over the other in this setting?
[glm]
Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) -3.4274 0.4641 -7.386 1.10e-12 ***
> capacity 0.9931 0.1281 7.754 9.55e-14 ***
> group2 0.7242 0.6337 1.143 0.25392
> group3 2.0264 0.6168 3.286 0.00112 **
> capacity:group2 -0.1523 0.1764 -0.863 0.38864
> capacity:group3 -0.3885 0.1742 -2.231 0.02633 *
[lmer]
> Generalized linear mixed model fit using Laplace
> Formula: prob.bind ~ capacity * group + (1 | subject)
> Subset: c(combination == "gnl")
> Family: quasibinomial(logit link)
[snip]
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) -3.8628 1.2701 -3.041
> capacity 1.1219 0.1176 9.542
> group2 0.9086 1.7905 0.507
> group3 2.3700 1.7936 1.321
> capacity:group2 -0.1745 0.1610 -1.083
> capacity:group3 -0.3807 0.1622 -2.348
[gee]
> Coefficients:
> Estimate Naive S.E. Naive z Robust S.E. Robust z
> (Intercept) -3.4798395 0.4910274 -7.0868545 0.4739913 -7.3415687
> capacity 1.0149659 0.1366365 7.4282170 0.1284162 7.9037210
> group2 0.7781014 0.6691731 1.1627806 0.7424769 1.0479807
> group3 2.0720270 0.6527565 3.1742727 0.6234005 3.3237495
> capacity:group2 -0.1750448 0.1877361 -0.9323982 0.2060484 -0.8495325
> capacity:group3 -0.4021872 0.1865916 -2.1554413 0.1724780 -2.3318168
>
I assume you're talking about the differences in the estimated standard
errors of the group3 (and group2) parameters (everything else looks pretty
similar)?
All else being equal I would trust lmer slightly more than gee (and the
non-clustered glm is not reliable for inference in this situation, since it
ignores the clustering) -- but I'm pretty ignorant of gee, so take that with
a grain of salt.
I would make the following suggestions --
1. consider whether it even makes sense to test the significance of the
group3 main effect in the presence of the capacity:group3 interaction. Is
the value capacity=0 somehow intrinsically interesting?
2. all of these standard error estimates are pretty crude/ rely on
large-sample assumptions (how big is your data set?); unfortunately more
sophisticated estimates of uncertainty are currently unavailable for GLMMs
in lmer. I would try your problem again with glmmML, just to check that it
gives similar answers to lmer.
3. if you need more advice, consider asking this on r-sig-mixed instead ...
Ben Bolker
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