nAGQ > 1 in lme4::glmer gives unexpected likelihood
Having said that, I do see that the fits in the MixedModels package for
Julia produce similar values of the deviance with the Laplace approximation
and nAGQ = 7
julia> m1 = fit(MixedModel, @formula(y ~ 1 + (1|group)), dd, Poisson())
Generalized Linear Mixed Model fit by maximum likelihood (nAGQ = 1)
y ~ 1 + (1 | group)
Distribution: Poisson{Float64}
Link: LogLink()
Deviance: 193.5587
Variance components:
Column Variance Std.Dev.
group (Intercept) 3.9577026 1.9893975
Number of obs: 100; levels of grouping factors: 10
Fixed-effects parameters:
??????????????????????????????????????????????????
Estimate Std.Error z value P(>|z|)
??????????????????????????????????????????????????
(Intercept) 2.65175 0.632317 4.19 <1e-4
??????????????????????????????????????????????????
julia> m1 = fit(MixedModel, @formula(y ~ 1 + (1|group)), dd, Poisson(),
nAGQ=7)
Generalized Linear Mixed Model fit by maximum likelihood (nAGQ = 7)
y ~ 1 + (1 | group)
Distribution: Poisson{Float64}
Link: LogLink()
Deviance: 193.5104
Variance components:
Column Variance Std.Dev.
group (Intercept) 3.9577026 1.9893975
Number of obs: 100; levels of grouping factors: 10
Fixed-effects parameters:
??????????????????????????????????????????????????
Estimate Std.Error z value P(>|z|)
??????????????????????????????????????????????????
(Intercept) 2.65175 0.632317 4.19 <1e-4
??????????????????????????????????????????????????
As the person who wrote the first version of the nAGQ code in R I would not
be surprised if there was a constant dropped somewhere. It is difficult
code.
And the results here in the Julia package make me uncomfortable because the
values of the parameter estimates are identical in the two fits. I would
expect them to be close but not identical.
Isn't it good to know that there is still room for research in this area?
:-)
On Fri, Apr 24, 2020 at 9:05 AM Douglas Bates <bates at stat.wisc.edu> wrote:
There's a lot of variability in your lambdas
exp(3 + random_effects)
[1] 91.5919358 6.9678749 4.1841478 78.0771666 890.6931394 20.8558107 [7] 3.0037864 0.3049416 2.1675995 40.6209684 Do you really expect that some groups will have a mean count of nearly 900 whereas others will have a mean count less than 1? On Wed, Apr 22, 2020 at 5:58 PM Ben Goldstein <ben.goldstein at berkeley.edu> wrote:
Hi all, I'm using lme4::glmer to estimate Poisson mixed models in a very simple context (single random effect). I'm interested in the model
likelihood/AIC
across many simulated datasets. To investigate whether the Laplace approximation was appropriate for my data context, I explored using the argument nAGQ to improve the accuracy
of
the likelihood estimation. When I changed nAGQ to a value > 1, I saw an unexpectedly huge change in the likelihood; log-likelihoods tended to be off by ~200. Other statistics packages (e.g. GLMMadaptive) yield
estimates
that agree with lme4's Laplace approximation, as did a manual likelihood
estimate, and not with the nAGQ > 2 estimate.
The following code reproduces the problem I'm encountering.
*# r-sig-mixed-models GLMM question*
library(lme4)
set.seed(51)
*# Simulate some random effect-driven Poisson data*
random_effects <- rnorm(10, 0, 2)
group <- rep(1:10, 10)
simulated_data <- data.frame(y = rpois(n = 100, lambda = exp(3 +
random_effects[group])),
group = group)
*# Fit models with Laplace (nAGQ = 1) and nAGQ = 11*
fit_Laplace <- glmer(y ~ (1|group), data = simulated_data, family =
poisson())
fit_AGQ <- glmer(y ~ (1|group), data = simulated_data, family =
poisson(),
nAGQ = 11) logLik(fit_Laplace) logLik(fit_AGQ) logLik(fit_Laplace) - logLik(fit_AGQ) *# Huge difference!* When I execute the above code, I see a difference in likelihood of -218.8894. I've tested across many simulations and on 2 different
machines
(Mac and Linux). My version of lme4 is up to date. Has anyone run into this issue before? Am I using the glmer function
wrong,
or is it possible there's something going on under the hood?
Thanks,
Ben
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