[R-sig-ME] gee, geese and glmer
Ming-Huei Chen
mhchen at bu.edu
Mon Mar 10 19:36:03 CET 2014
Thanks, Ben!
Changing optimization method does not change results for lme4 1.0-6. I will
install 1.1-4 version and get back to you.
Best,
Ming-Huei
-----Original Message-----
From: Ben Bolker [mailto:bbolker at gmail.com]
Sent: Saturday, March 8, 2014 5:25 PM
To: r-sig-mixed-models at r-project.org
Cc: Ming-Huei Chen; 'Yang, Qiong'
Subject: Re: gee, geese and glmer
On 14-03-07 11:25 PM, Ming-Huei Chen wrote:
> Hi Ben,
>
>
>
> In an analysis we found that glmer in new lme4 gave result different
> from old lme4, gee and geese, where old lme4 seems to be closer to gee
> and geese.. Please see highlighted sex effect below. Case by sex (2x2)
> table is also given. Can you please let us know how would you look
> into the results? Thanks!
>
[cc'ing to r-sig-mixed-models: **please** try r-sig-mixed-models first,
not personal e-mail to me ...]
I can't say exactly what's going here; without having a reproducible
example <http://tinyurl.com/reproducible-000> it's hard to say precisely.
Thoughts:
* gee and geese are giving _exactly_ the same parameter estimates, to
8 significant digits, so I would guess they are wrapping identical
underlying methods.
* As far as diagnosing the issue with lme4 1.0-6:
* does changing the optimization method, i.e.
glmerControl(optimizer="optimx",optCtrl=list(method="nlminb"))
[must do library("optimx") first] or
glmerControl(optimizer="bobyqa")
change the result?
* I would be curious whether the soon-to-be-released version 1.1-4 (which
can be installed from github or lme4.r-forge.r-project.org/repos) gives
either (1) convergence warnings or (2) different/better answers
* You can try specifying the starting values for lme4 to diagnose
misconvergence; for example, start lme4 from the estimates given by old
lme4/lme4.0 and see if it gives a similar answer.
* You can use the 'slice' and 'splom.slice' functions from bbmle to
visualize the likelihood surfaces
good luck,
Ben Bolker
> Ming-Huei
>
> ###GEE
>
>> summary(gee(case~sex+PC1+PC2+PC3+PC4,id=famid,family=binomial,data=da
>> ta))$coef
> Estimate Naive S.E. Naive z Robust S.E. Robust z
> (Intercept) -1.88047373 0.13532162 -13.8963286 0.15960440 -11.782092
> sex -0.23436854 0.08611269 -2.7216494 0.09050577 -2.589543
> PC1 -0.05478639 0.06195318 -0.8843192 0.06822178 -0.803063
> PC2 -0.09934572 0.06494563 -1.5296753 0.06520811 -1.523518
> PC3 -0.07020391 0.06626875 -1.0593818 0.06962147 -1.008366
> PC4 -0.13413097 0.06746716 -1.9880927 0.06979901 -1.921674
>
> ###GEESE
>
>> summary(geese(case~sex+PC1+PC2+PC3+PC4,id=famid,family=binomial,data=
>> data))$mean
>
> estimate san.se wald p
>
> (Intercept) -1.88047373 0.15960440 138.8176912 0.000000000
> sex -0.23436854 0.09050577 6.7057312 0.009610351
> PC1 -0.05478639 0.06822178 0.6449102 0.421938319
> PC2 -0.09934572 0.06520811 2.3211071 0.127629159
> PC3 -0.07020391 0.06962147 1.0168016 0.313278888
> PC4 -0.13413097 0.06979901 3.6928324 0.054646745
>
> ### lme4_0.999999-2
>
>> summary(glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomial,data
>> =data))
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -3.01599 0.28305 -10.655 <2e-16 ***
> sex -0.41056 0.16285 -2.521 0.0117 *
> PC1 -0.17116 0.12903 -1.326 0.1847
> PC2 -0.15510 0.13382 -1.159 0.2465
> PC3 -0.19044 0.13580 -1.402 0.1608
> PC4 0.02532 0.13732 0.184 0.8537
>
> ###lme4_1.0-6
>
>> summary(glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomial,data
>> =data))
>
> Estimate Std. Error z value Pr(>|z|)
>
> (Intercept) -10.2784 0.8631 -11.909 <2e-16 ***
> sex 0.3497 0.1975 1.770 0.0767 .
> PC1 -0.3555 0.1623 -2.190 0.0285 *
> PC2 -0.1087 0.1653 -0.657 0.5109
> PC3 -0.2242 0.1652 -1.357 0.1748
> PC4 0.1103 0.1671 0.660 0.5091
>
> Case by sex
>
> 1 2
> 0 2554 3021
> 1 310 290
>
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