-----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$ID <- factor(rep(letters, length.out = 1000))
m <- MCMCglmm(vs ~ hp, random = ~ idh(1 + hp):ID, family = "categorical",
data = dat, prior = list(
B = list(mu = c(0, 0), V = diag(2) * 1e10),
R = list(V = 1, fix = 1),
G = list(G1 = list(V = diag(2), nu = .002))), pr=TRUE,
nitt = 55000, thin = 100, burnin = 5000, verbose=FALSE)
Just up the number of iterations if you want more precision. Jarrod
Hadfield's course notes are a great introduction.
Cheers,
Josh
On Sun, Jul 14, 2013 at 5:04 AM, Viechtbauer Wolfgang (STAT)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Thanks for the reply. I wasn't aware of the fact that lme4.0/old-lme4
only allowed one grouping variable with nAGQ > 1, but I just tried that
out and that is indeed the case. My more pressing concern with the new
lme4 however is the possibility of allowing for nonscalar random effects
terms. I frequently fit logistic regression models with multiple random
effects (like a random intercept for individuals/clusters and a random
effect on a dummy variable to allow for variable treatment effects). It
would be great to still benefit from the increased accuracy of nAGQ > 1
then. It would be nice if that could be put on the to-do list, but I know
from personal experience how those to-do lists have a tendency just to get
longer than shorter over time.
-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-
bounces at r-project.org] On Behalf Of Ben Bolker
Sent: Thursday, July 11, 2013 17:36
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] glmer with nAGQ > 1
Viechtbauer Wolfgang (STAT <wolfgang.viechtbauer at ...> writes:
I just tried to fit a mixed-effects logistic regression model with
version 0.99999911-5 of lme4 (installed from github). The model
includes a random effect for clusters and a random group/treatment
effect. I received the following error:
Error in updateGlmerDevfun(devfun, glmod$reTrms, nAGQ = nAGQ) : nAGQ
> 1 is only available for models with a single, scalar
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