Sorry for double posting. But last Friday I forgot to change the subject line. This is still about the nAGQ maximum. Thanks for your replies. I am aware that 25 is already a large number of quadrature points for a scalar random effect. I had one particular example in mind when asking you about this 25 quadrature points. It is the toenail data discussed for example in section 14.8 (p. 278) of "Models for discrete longitudinal data" (Molenberghs, Verbeke, 2005).This data is available on http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN% 291467-9876/homepage/50_3.htm or in the glmmAK package. As illustrated with the code below, when using just a scalar random intercept the differences in the estimates is not very large. Though it looks different when using a random intercept and a random slope: library(lme4) packageVersion("lme4") # [1] ?0.999999.2? library(glmmAK) data(toenail) RI1 <- glmer(infect~trt+time+trt:time+(1|idnr), data=toenail, family=binomial, nAGQ=25) RI2 <- glmer(infect~trt+time+trt:time+(1|idnr), data=toenail, family=binomial, nAGQ=50) fixef(RI2)-fixef(RI1) RS1 <- glmer(infect~trt+time+trt:time+(1+time|idnr), data=toenail, family=binomial, nAGQ=25) RS2 <- glmer(infect~trt+time+trt:time+(1+time|idnr), data=toenail, family=binomial, nAGQ=50) fixef(RS2)-fixef(RS1) The fixed effect intercept differs by 0.139 when using nAGQ=25 or nAGQ=50. For the model with a random intercept only the differences reduce to the third digit. Comparable differences can be found when using NLMMIXED in SAS. I don't know if this is a large difference or not but the point is that I assume from this example that with even more complicated random effects the upper limit of 25 might be too low. Or is it just generally a bad idea to fit GLMM models which still have varying estimates with nAGQ=25? So my point is that the maximum at 25 should perhaps be reconsidered when it comes to implementing non-scalar random effects. Rafael Sauter On Fri, 2013-09-27 at 12:00 +0200,
r-sig-mixed-models-request at r-project.org wrote:
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https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models or, via email, send a message with subject or body 'help' to r-sig-mixed-models-request at r-project.org You can reach the person managing the list at r-sig-mixed-models-owner at r-project.org When replying, please edit your Subject line so it is more specific than "Re: Contents of R-sig-mixed-models digest..." Today's Topics: 1. Re: Maximum nAGQ=25? (Ben Bolker) ---------------------------------------------------------------------- Message: 1 Date: Thu, 26 Sep 2013 21:21:33 +0000 (UTC) From: Ben Bolker <bbolker at gmail.com> To: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] Maximum nAGQ=25? Message-ID: <loom.20130926T231632-517 at post.gmane.org> Content-Type: text/plain; charset=us-ascii Ross Boylan <ross at ...> writes: On Thu, Sep 26, 2013 at 04:23:47PM +0000, Ben Bolker wrote: Rafael Sauter <rafael.sauter <at> ...> writes: [snip] As I did not find any discussion about this change in the new lme4-version let me allow to ask: 1) Why is 25 a reasonable upper bound for nAGQ? What were the reasons to implement this upper bound? Is the increasing complexity as mentioned in the details of '?glmer' the the main reason for this? [snip] I will only speak for myself: other lme4-authors (especially Doug Bates) may chime in on this one. I believe there isn't a rigorous argument for why >25 quadrature points is too many: ?glmer says " A model with a single, scalar random-effects term could reasonably use up to 25 quadrature points per scalar integral." [snip] I think we would certainly be willing to reconsider this limit if you can show that there is some sensible case where it matters ... If the limit is hard-coded to 25, it will be hard to discover if using 25 matters. That seems to me an argument for not hard coding it. I suppose if the results had not stabilized by 25 that would be an indication. OTOH, 25 is a lot of quadrature points. One problem I've encountered with high number of quadrature points--not in lmer, but I think it's a general issue--is that as the number of quadrature points goes up the extreme x values go up, and numerical problems are more likely. Usually one can compensate by coding the likelihood defensively. Ross Boylan As may have been reflected in discussions on the list, the lme4 authors have been having lots of internal discussions about how much flexibility to allow, when to give users helpful advice in the form of warnings, etc etc etc.. Not completely tongue-in-cheek, I could say that if you're capable of compiling a package from source, it's not very complicated to search for "nAGQ <= 25L" in R/modular.R and modify or remove this limitation for yourself ... Ben Bolker ------------------------------ _______________________________________________ R-sig-mixed-models mailing list R-sig-mixed-models at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models End of R-sig-mixed-models Digest, Vol 81, Issue 51 **************************************************