[R-sig-ME] lme4 convergence/numerical issue with large sample 2 level logit

Daniel Adkins deadkins at vcu.edu
Sat May 28 00:19:03 CEST 2011 

Sorry for cluttering up the listserve, hopefully this is the last one.

i have successfully installed "lme4a". however it ignores any "nAGQ"
syntax and gives laplace approx-based solution. this soultion is
obviously a bad local optimum. is there any way to set quadrature
point >1 using this package?

Thx!
D

On Fri, May 27, 2011 at 4:44 PM, Daniel Adkins <deadkins at vcu.edu> wrote:
> Dennis,
> Thank you for your help. I save installed the "Matrix" package several
> different ways (until i am currently blue in the face) and still
> receive the following error:
>
>> library(lme4a)
> Error: package 'Matrix' required by 'lme4a' could not be found
>
> Thoughts?
>
> Best,
> D
>
> On Fri, May 27, 2011 at 4:16 PM, Dennis Murphy <djmuser at gmail.com> wrote:
>> Hi:
>>
>> Re installation of lme4a on Win7, there are at least two ways to do it:
>>
>> (1) Within an R session, copy and paste the following:
>> install.packages("lme4a", repos="http://R-Forge.R-project.org")
>> install.packages("Matrix", repos="http://R-Forge.R-project.org")
>>
>> (2) Go to  http://r-forge.r-project.org/bin/windows/contrib/latest/
>>     and download the latest binaries for lme4a and Matrix as zip files
>>
>>     In R, go to 'Install packages from local zip files' from the
>> Packages menu in Rgui, find the two zip files  and then install them.
>> You need the development version of Matrix on R-forge for
>> compatibility with lme4a (true at least up to May 15 or so, but double
>> check). The last time I installed it, I got an error on startup that
>> was eliminated when I upgraded Matrix to the development version on
>> R-forge; that's why I suggest installing both at the same time. Here
>> are the versions of each that I have:
>>
>> < from sessionInfo() >
>> other attached packages:
>>  [1] lme4a_0.999375-66  MatrixModels_0.2-1 minqa_1.1.15       Rcpp_0.9.4
>>  [5] Matrix_0.9996875-0 rJava_0.8-8        sos_1.3-0          brew_1.0-6
>>  [9] lattice_0.19-26    ggplot2_0.8.9      proto_0.3-9.2      reshape_0.8.4
>> [13] plyr_1.5.2
>>
>> AFAICT, both are working fine in concert and both methods mentioned
>> above are equivalent; the difference is that method (2) requires more
>> work on your part :)
>>
>> I'm cc-ing this to Doug just in case he's not aware of the 'fix' on
>> Win 7 systems for lme4a (he most likely does, but better to be safe).
>>
>> Happy mixed modeling :)
>> Dennis
>>
>> On Fri, May 27, 2011 at 12:35 PM, Daniel Adkins <deadkins at vcu.edu> wrote:
>>> doug,
>>> thanks for your reply. i do tons of mixed modeling, so obviously am a
>>> big fan of / highly dependent on your work.
>>>
>>> results from:
>>>>sessionInfo()
>>>
>>> R version 2.13.0 (2011-04-13)
>>> Platform: i386-pc-mingw32/i386 (32-bit)
>>>
>>> locale:
>>> [1] LC_COLLATE=English_United States.1252
>>> [2] LC_CTYPE=English_United States.1252
>>> [3] LC_MONETARY=English_United States.1252
>>> [4] LC_NUMERIC=C
>>> [5] LC_TIME=English_United States.1252
>>>
>>> attached base packages:
>>> [1] stats     graphics  grDevices utils     datasets  methods   base
>>>
>>> other attached packages:
>>>  [1] MCMCglmm_2.12      corpcor_1.5.7      ape_2.7-1          coda_0.14-4
>>>  [5] tensorA_0.36       lme4_0.999375-39   Matrix_0.999375-50 lattice_0.19-23
>>>  [9] nlme_3.1-100       foreign_0.8-43
>>>
>>> loaded via a namespace (and not attached):
>>> [1] gee_4.13-16   grid_2.13.0   stats4_2.13.0 tools_2.13.0
>>>
>>> i apologize for trying MCMCglmm, feel like i have lipstick on my collar (ha!).
>>>
>>> Please do tell me how to install "lme4a", I have downloaded the zip
>>> file for windows
>>> (https://r-forge.r-project.org/bin/windows/contrib/latest/lme4a_0.999375-66.zip),
>>> but haven't figured out how to load it into working session of R.
>>>
>>> also, could you point me in the right direction for start values?
>>> would it appreciably help optimization to use results from random
>>> intercept only model as starts for fixed effects and random intercept
>>> variance estimate?
>>>
>>> also, what do you think of "glmmPQL"? does that yield decent estimates
>>> for models of my level of size/complexity?
>>>
>>> Thx for any help.
>>> D
>>>
>>>
>>> On Fri, May 27, 2011 at 2:14 PM,  <dmbates at gmail.com> wrote:
>>>> On Thu, May 26, 2011 at 8:53 PM, Daniel Adkins <deadkins at vcu.edu> wrote:
>>>>> hi all,
>>>>> trying to fit either this model:
>>>>>
>>>>> proto1 <- lmer(hibpe ~ age + b + b_age + h + h_age + female +
>>>>> female_age + bxf + hxf + numwaves + dead + nodoctor + nohosp
>>>>>        +(1|hhidpn) +(0 + age | hhidpn) ,  nAGQ =300, family=binomial,
>>>>> data=hrs_data, na.action =na.omit,  verbose=TRUE)
>>>>>
>>>>> or the same model allowing correlation btwn the REs:
>>>>>
>>>>> proto2 <- lmer(hibpe ~ age + b + b_age + h + h_age + female +
>>>>> female_age + bxf + hxf + numwaves + dead + nodoctor + nohosp
>>>>>        + (age | hhidpn) ,  nAGQ =300, family=binomial, data=hrs_data,
>>>>> na.action =na.omit,  verbose=TRUE)
>>>>>
>>>>> it is big data: ~50K obs & ~8400 clusters (subjects, in this case).
>>>>>
>>>>> when i try to fit these models they run ~forever (>72 hours) with no
>>>>> convergence. when i try a laplace approx for the quadrature (i.e.,
>>>>> nAGQ=1) I get false convergence.
>>>>>
>>>>> I can successfully fit a slightly less complex model in which
>>>>> everything is the same except dropping the age RE:
>>>>>
>>>>> proto2 <- lmer(hibpe ~ age + b + b_age + h + h_age + female +
>>>>> female_age + bxf + hxf + numwaves + dead + nodoctor + nohosp
>>>>>        + (1 | hhidpn) ,  nAGQ =300, family=binomial, data=hrs_data,
>>>>> na.action =na.omit,  verbose=TRUE)
>>>>>
>>>>> this takes ~25 minutes to converge, and it gives an accurate solution
>>>>> (cross-validated in stata via xtlogit and (for fixed effects) using
>>>>> simple, single level logit). notably, i only get global optimum
>>>>> convergence once I set nAGQ>200.
>>>>
>>>> nAGQ > 200 ?  That seems rather drastic when you have an average of 6
>>>> observations per subject.
>>>>
>>>> The problem may be related to the optimizer.  The development version
>>>> of the package, called lme4a, uses another optimizer that has, in most
>>>> but not all cases, been faster and more reliable.  You may want to try
>>>> that version of the package instead.  If you can tell us what platform
>>>> you are using, say by providing the output of
>>>>
>>>> sessionInfo()
>>>>
>>>> we should be able to provide you with installation instructions for
>>>> lme4a (although I think that installation on Mac OS X is still not
>>>> straightforward).
>>>>
>>>>
>>>>> Obviously, the estimation of the 2nd RE is the issue and a very
>>>>> difficult numerical problem given data size/structure. Stata
>>>>> (xtmelogit) just conks out almost immediately and says that it doesn't
>>>>> have the memory to handle the quadrature optimization (not in so many
>>>>> words, but still), even with memory maxed out for the
>>>>> program/hardware.
>>>>>
>>>>> Any suggestions for shortcuts to make 'proto1' or 'proto2' estimate? i
>>>>> am about to have to resort to BUGS, which will take eons, but at least
>>>>> will eventually give decent estimates. Would strongly prefer sticking
>>>>> with R though. Start values maybe? Would that help? Any code for
>>>>> inputting these (i have never done it before in R)? nlme? hglm? I am
>>>>> desperate/open to any suggestions.
>>>>>
>>>>> Thx,
>>>>> D
>>>>>
>>>>>
>>>>> --
>>>>> Daniel E. Adkins, PhD
>>>>> Assistant Professor
>>>>> Center for Biomarker Research and Personalized Medicine
>>>>> School of Pharmacy
>>>>> Virginia Commonwealth University
>>>>> McGuire Hall, Room 216B
>>>>> 1112 East Clay Street
>>>>> Richmond, VA 23298
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-mixed-models at r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>
>>>>
>>>
>>>
>>>
>>> --
>>> Daniel E. Adkins, PhD
>>> Assistant Professor
>>> Center for Biomarker Research and Personalized Medicine
>>> School of Pharmacy
>>> Virginia Commonwealth University
>>> McGuire Hall, Room 216B
>>> 1112 East Clay Street
>>> Richmond, VA 23298
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>
>
>
> --
> Daniel E. Adkins, PhD
> Assistant Professor
> Center for Biomarker Research and Personalized Medicine
> School of Pharmacy
> Virginia Commonwealth University
> McGuire Hall, Room 216B
> 1112 East Clay Street
> Richmond, VA 23298
>



-- 
Daniel E. Adkins, PhD
Assistant Professor
Center for Biomarker Research and Personalized Medicine
School of Pharmacy
Virginia Commonwealth University
McGuire Hall, Room 216B
1112 East Clay Street
Richmond, VA 23298

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