[R-sig-ME] glmer with nAGQ > 1

[Viechtbauer Wolfgang (STAT)]

Hi Josh,

Thank you for the suggestion and the code. Very useful!

Best,
Wolfgang

> -----Original Message-----
> From: Joshua Wiley [mailto:jwiley.psych at gmail.com]
> Sent: Monday, July 15, 2013 19:59
> To: Viechtbauer Wolfgang (STAT)
> Cc: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] glmer with nAGQ > 1
> 
> Hi Wolfgang,
> 
> Another good option would be to use MCMCglmm.  It could look something
> like:
> 
> set.seed(1234)
> dat <- mtcars[sample(1:32, 1000, replace = TRUE), ]
> dat <- within(dat, {
>   qsec <- scale(qsec)
>   hp <- scale(hp)
>   mpg <- scale(mpg)
>   disp <- scale(disp)
> })
> dat$ID <- factor(rep(letters, length.out = 1000))
> 
> m <- MCMCglmm(vs ~ hp, random = ~ idh(1 + hp):ID, family = "categorical",
>   data = dat, prior = list(
>   B = list(mu = c(0, 0), V = diag(2) * 1e10),
>   R = list(V = 1, fix = 1),
>   G = list(G1 = list(V = diag(2), nu = .002))), pr=TRUE,
>   nitt = 55000, thin = 100, burnin = 5000, verbose=FALSE)
> 
> Just up the number of iterations if you want more precision.  Jarrod
> Hadfield's course notes are a great introduction.
> 
> Cheers,
> 
> Josh
> 
> 
> On Sun, Jul 14, 2013 at 5:04 AM, Viechtbauer Wolfgang (STAT)
> <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
> > Thanks for the reply. I wasn't aware of the fact that lme4.0/old-lme4
> only allowed one grouping variable with nAGQ > 1, but I just tried that
> out and that is indeed the case. My more pressing concern with the new
> lme4 however is the possibility of allowing for nonscalar random effects
> terms. I frequently fit logistic regression models with multiple random
> effects (like a random intercept for individuals/clusters and a random
> effect on a dummy variable to allow for variable treatment effects). It
> would be great to still benefit from the increased accuracy of nAGQ > 1
> then. It would be nice if that could be put on the to-do list, but I know
> from personal experience how those to-do lists have a tendency just to get
> longer than shorter over time.
> >
> > Best,
> > Wolfgang
> >
> >> -----Original Message-----
> >> From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-
> models-
> >> bounces at r-project.org] On Behalf Of Ben Bolker
> >> Sent: Thursday, July 11, 2013 17:36
> >> To: r-sig-mixed-models at r-project.org
> >> Subject: Re: [R-sig-ME] glmer with nAGQ > 1
> >>
> >> Viechtbauer Wolfgang (STAT <wolfgang.viechtbauer at ...> writes:
> >>
> >> > I just tried to fit a mixed-effects logistic regression model with
> >> > version 0.99999911-5 of lme4 (installed from github). The model
> >> > includes a random effect for clusters and a random group/treatment
> >> > effect. I received the following error:
> >>
> >> > Error in updateGlmerDevfun(devfun, glmod$reTrms, nAGQ = nAGQ) : nAGQ
> >> >   > 1 is only available for models with a single, scalar
> >> >   random-effects term
> >>
> >> > Indeed, I had set nAGQ > 1 to get more precision with the evaluation
> >> > of the integrals via Gauss-Hermite quadrature. It's clear what the
> >> > error message says, but I am wondering if this is going to be a
> >> > permanent design choice or something temporary.
> >>
> >>    It's probably a "foreseeable future" decision (alas).
> >>
> >>    I don't know the guts of the AGQ calculation tremendously well,
> >> so I don't know exactly what would be involved in constructing
> >> a multi-dimensional AGQ.  Taking a brief look back at lme4.0/old-lme4,
> >> it seems that only a single _grouping variable_ was allowed, but
> >> it was not limited to scalar random effects terms, so it might
> >> not be too horrible to re-implement ... but it's not on the
> >> urgent "to do" list at the moment ...  (Anyone want to volunteer
> >> to take a look at the code and implement this ???)
> >>
> >>   Ben