From: Daniel Malter <daniel_at_umd.edu>

Date: Mon, 07 Jul 2008 17:24:13 -0400

cuncta stricte discussurus

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Date: Mon, 07 Jul 2008 17:24:13 -0400

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?

(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 -0.074I(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: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org] Im
Auftrag von Ben Bolker

Gesendet: Monday, July 07, 2008 1:41 PM

An: r-help_at_stat.math.ethz.ch

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 --

- 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?
- 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.
- if you need more advice, consider asking this on r-sig-mixed instead ...

Ben Bolker

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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. Received on Mon 07 Jul 2008 - 21:28:03 GMT

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