# [R-sig-ME] nAGQ > 1 in lme4::glmer gives unexpected likelihood

Vaida, Florin |v@|d@ @end|ng |rom he@|th@uc@d@edu
Sat Apr 25 16:41:01 CEST 2020

```Looks like you guys thought about deviance issues a lot:
https://github.com/lme4/lme4/issues/375

Speaking about the principle of "least surprise", it is surprising to get completely different behaviors in deviance for nAGQ=1 vs nAGQ=2 (say), in the same model - (what Ben Goldstein pointed out).

A couple further questions:
1. Is the "off by" constant the same, for a given model, if I only change nAGQ?  E.g., comparing nAGQ=5 and nAGQ=20?
2. If so, can you calculate the "off by" constant by comparing with formula-based Laplace approximation with a bona fide Adaptive Gaussian Quadrature using existing algorithm but n=1?

Florin

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

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}

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}

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 using stat.wisc.edu<mailto:bates using stat.wisc.edu>> wrote:

There's a lot of variability in your lambdas

exp(3 + random_effects)
  91.5919358   6.9678749   4.1841478  78.0771666 890.6931394
20.8558107
   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 using berkeley.edu<mailto:ben.goldstein using 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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