lme4 convergence/numerical issue with large sample 2 level logit

library(lme4a)

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

--
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