gee, geese and glmer
Hi Ben and Martin,
Thanks you for your reply and taking time to explain the situation.
Maybe I wasn't clear in my previous message or don't have a deeper understanding of the CRAN policy: Instead of separate packages for "old-CRAN" lme4 (lme4.0), and the current lme4, which is not allowed by CRAN maintainer, is it possible to have an option in current lme4 to call the algorithm in old lme4?
I thought the new lme4 implements a different algorithm and why not make the old algorithm available in current lme4 lmer() as an option. It is like we can request pearson or spearman correlations in cor(). Sorry if I simplified the problem too much.
To Ben, Sorry that we cannot share data because of confidentiality policy. But you are able to debug our problem through PC screen sharing where you can gain control and run programs on our Linux
session without possessing the data. If that sounds like a good idea, let us know when you might be available to do so.
Thanks,
Qiong
-----Original Message-----
From: Ben Bolker [mailto:bbolker at gmail.com]
Sent: Tuesday, March 11, 2014 9:26 AM
To: Martin Maechler; Yang, Qiong
Cc: r-sig-mixed-models at r-project.org; Chen, Ming-Huei
Subject: Re: [R-sig-ME] gee, geese and glmer
I would also point out that we are indeed very interested, in the medium term (the short term is very very busy!), in making sure that your issues are resolved. We would like lme4 to dominate lme4.0 (i.e., to work better in all circumstances). So far it's been a bit difficult since we have been debugging remotely -- short of the suggestions I gave below, and without access to a reproducible example, it's very hard indeed for me to say much more.
sincerely
Ben Bolker
On 14-03-11 05:45 AM, Martin Maechler wrote:
Yang, Qiong <qyang at bu.edu>
on Mon, 10 Mar 2014 18:19:56 +0000 writes:
> Hi Ben, We wonder if you can add an option in lmer() of
> current lme4 version to call the algorithm used in
> lme4_0.999999-2?
unfortunately not. For this reason, we had planned for many months, starting in August 2012 and announced on this mailing list at least a year ago that we would provide the 'lme4.0' package (back-compatible as well as possible in light of computer OS updates incl system libraries, and R updates, ...) in order to provide useRs the possibility of reproducible research and data analysis for their analyses done with "old-CRAN" lme4 versions. Unfortunately (for us), the CRAN maintainers decided that providing lme4.0 in addition to lme4 was a bad idea, and explicit forbid such actions in the (then new) CRAN policy document. I'm still not at all happy with that decision.
> For our package (analyze rare genetic variant) to be put
> on CRAN, we need to use current version of lme4. However,
> at this point, there are still issues that cannot be
> resolved with newer versions of lme4. It is very difficult
> resolved with newer versions of lme4. It is very difficult
> for us to keep waiting and testing the new release, and
> hope all the issues resolved and no new issues coming
> up. lme4_0.999999-2 has been used by us for a long time
> with little problem.
Good to hear. Such cases were exactly the reason why we (lme4 authors) made such considerable effort to provide lme4.0 for reproducible research and data analysis. at the bottom of https://github.com/lme4/lme4/blob/master/README.md we mention the state and give installation instruction of lme4.0, but as you say, this does not solve the problem for other package maintainers: If they want a CRAN package, they (currently? I'm optimistic beyond reason :-) cannot have a 'Depends: lme4.0' (or "Imports:..." or similar).
> Your help on this is highly
> appreciated. Best, Qiong
You're welcome; currentl there's not more we can do. Martin -- Martin Maechler, ETH Zurich, Switzerland
> -----Original Message----- From: Ben Bolker
> [mailto:bbolker at gmail.com] Sent: Saturday, March 08, 2014
> 5:25 PM To: r-sig-mixed-models at r-project.org Cc: Chen,
> Ming-Huei; 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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