[R-sig-ME] lme4 convergence/numerical issue with large sample 2 level logit
Daniel Adkins
deadkins at vcu.edu
Fri May 27 22:44:18 CEST 2011
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
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