[R-sig-ME] nAGQ = 0
jdpo223 at g.uky.edu
Mon Sep 4 00:59:30 CEST 2017
Oh and on the model comparison thing the only way i know how to directly
compare random effects distribution estimates is with gateaux derivatives a
la what Sophia Rabe-Hesketh did in her GLLAMM software for nonparametric
random effects estimation.
Usually i just kind of eyeball it and try to be overly conservative with
quadrature points or use MCMC.
I guess you could rig up some regular deviance test to do the same thing?
On Sep 3, 2017 6:51 PM, "Poe, John" <jdpo223 at g.uky.edu> wrote:
> I'm pretty sure that nAGQ=0 is generating conditional modes of group
> values for the random effects without subsequently using a laplace
> approximation. This is really not something that you want to do unless it's
> a last resort. It's kind of like estimating a linear probability model with
> a random intercept and using the values for that in the cloglog model. My
> understanding is that it's not even an option in most other software.
> Someone please correct me if I'm wrong here because what I've found on it
> has been kind of vague and I'm making some assumptions.
> My guess is that the random intercepts/slopes are going to be too small
> and their distributions could be distorted if you're not actually
> approximating the integral with something like quadrature or mcmc. That's
> the case with PQL and MQL according to simulation evidence at least and
> even though this isn't the same as PQL I'd expect a similar problem at
> first blush.
> As to if this is a real practical problem or a theory problem there's
> been kind of a disagreement on that within the stats literature. Some
> people argue, in binary outcome models, that biased random intercepts can
> bias everything else and others have argued this fear is overblown. This
> might well have been settled by actual statisticians by now, I'm not sure.
> It's gotten enough attention in the literature that people certainly
> worried about it a lot.
> Below are two articles on the topic with simulations but I've seen the
> fixed effects results (and LR tests) change based on the random effects
> approximation technique in my work so I'm always a bit paranoid about it.
> Model misspecification and having oddly shaped random intercepts (as with
> count models) can seem to make this problem worse.
> You can try using the BRMS package if you aren't comfortable switching to
> something totally unfamiliar. It's a wrapper for Stan designed to use lme4
> syntax and a lot of good default settings. It's pretty easy to use if you
> know lme4 syntax and can read up on mcmc diagnostics.
> Litière, S., et al. (2008). "The impact of a misspecified random‐effects
> distribution on the estimation and the performance of inferential
> procedures in generalized linear mixed models." Stat Med 27(16): 3125-3144.
> McCulloch, C. E. and J. M. Neuhaus (2011). "Misspecifying the shape of a
> random effects distribution: why getting it wrong may not matter."
> Statistical Science: 388-402.
> On Sep 3, 2017 5:49 PM, "Rolf Turner" <r.turner at auckland.ac.nz> wrote:
>> On 04/09/17 03:48, Jonathan Judge wrote:
>>> I have not studied this extensively with smaller datasets, but with
>>> larger datasets --- five-figure and especially six-figure n --- I have
>>> found that it often makes no difference.
>> When uncertain, I have used a likelihood ratio test to see if the
>>> differences are likely to be material.
>> My overall suggestion would be that if the dataset is small enough
>>> for this choice to matter, it is probably also small enough to solve the
>>> model through MCMC, in which case I would recommending using that,
>>> because the incorporated uncertainty often gives you better parameter
>>> estimates than any increased level of quadrature.
>> Thanks Jonathan.
>> (a) How small is "small"? I have 3 figure n's. I am currently mucking
>> about with two data sets. One has 952 observations (with 22 treatment
>> groups, 3 random effect reps per group). The other has 142 observations
>> (with 6 treatment groups and again 3 reps per group). Would you call the
>> latter data set small?
>> (b) I've never had the courage to try the MCMC approaches to mixed
>> models; have just used lme4. I guess it's time that I bit the bullet.
>> Psigh. This is going to take me a while. As an old dog I *can* learn
>> new tricks, but I learn them *slowly*. :-)
>> (c) In respect of the likelihood ratio test that you suggest --- sorry to
>> be a thicko, but I don't get it. It seems to me that one is fitting the
>> *same model* in both instances, so the "degrees of freedom" for such a test
>> would be zero. What am I missing?
>> Thanks again.
>> Technical Editor ANZJS
>> Department of Statistics
>> University of Auckland
>> Phone: +64-9-373-7599 ext. 88276
>> R-sig-mixed-models at r-project.org mailing list
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