nAGQ > 1 in lme4::glmer gives unexpected likelihood

 ? I think they're both 'correct' in the sense of being proportional to 
the likelihood but use different baselines, thus incommensurate (see the 
note pointed out below).

On 4/24/20 5:05 PM, Vaida, Florin wrote:

One can figure out which likelihood version is correct (Laplace or nAGQ>1) by doing a simulation with random effects variance -> 0.
I'll do that if I find time.
Florin

On Apr 24, 2020, at 7:59 AM, Ben Bolker <bbolker at gmail.com> wrote:


    I found the note I was looking for.  In ?deviance.glmerMod it says:

  If adaptive Gauss-Hermite quadrature is used, then
           ?logLik(object)? is currently only proportional to the
           absolute-unconditional log-likelihood.

(see the discussion on this page for more context); see also https://github.com/lme4/lme4/blob/master/misc/logLikGLMM/logLikGLMM.R


On 4/24/20 10:24 AM, Douglas Bates wrote:

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

         [[alternative HTML version deleted]]

         [[alternative HTML version deleted]]

	[[alternative HTML version deleted]]