discrepancies in lme4

The change in lme4 version 1.1-4 does explain the changes in the 
standard errors of fixed effects in our models, but there are some 
exceptions where the output suggests, that these values are not
computed correctly.

For example, I have a model where the output seems reasonably close
to the older computations:

Generalized linear mixed model fit by maximum likelihood (Laplace 
Approximation) [glmerMod] Family: gaussian  ( inverse ) Formula:
Area1_ha ~ Light * tel_round + SEX + (1 | Enclosure/animal) Data:
kern

Fixed effects: Estimate Std. Error t value Pr(>|z|) (Intercept)
16.745      3.611   4.638 3.52e-06 *** Light1              -1.010
4.023  -0.251   0.8017 tel_round2           0.442      1.695
0.261   0.7943 SEX1                 1.818      2.944   0.618
0.5368

Light1:tel_round2   -6.996      2.860  -2.446   0.0144 *


But after model simplification, where only one fixed factor is
omitted, the output is extremely different. All the calculated
standard errors are very small and similar leading to
unrealistically high p-values which do not correspond at all to
older computations:

Generalized linear mixed model fit by maximum likelihood (Laplace 
Approximation) [glmerMod] Family: gaussian  ( inverse ) Formula:
Area1_ha ~ Light * tel_round + (1 | Enclosure/animal) Data: kern

Fixed effects: Estimate Std. Error t value Pr(>|z|) (Intercept)
17.748296   0.006443  2754.5   <2e-16 *** Light1
-0.345857   0.006444   -53.7   <2e-16 *** tel_round2
0.455012   0.006445    70.6   <2e-16 *** Light1:tel_round2
-7.368427   0.006445 -1143.3   <2e-16 ***

Do you know what could be the problem in the calculation of the
standard errors in the second model although it is so similar to
the first one?


Thank you for your help, Julia Hoffmann

On Mon, 15 Jun 2015 09:47:08 -0400 Ben Bolker <bbolker at gmail.com>
wrote:

I strongly suspect this is related to the following change in
lme4 version 1.1-4:

\item Standard errors of fixed effects are now computed from the 
approximate Hessian by default (see the \code{use.hessian}
argument in \code{vcov.merMod}); this gives better (correct)
answers when the estimates of the random- and fixed-effect
parameters are correlated (Github #47)

(see https://github.com/lme4/lme4/blob/master/inst/NEWS.Rd )

This should apply only to your glmer results, not to lmer
results.

To check this, try summary(fitted_model,use.hessian=FALSE); it 
should give you the old results.

A further check would be to run
confint(fitted_model,parm="beta_"), which will give you more
reliable likelihood profile confidence intervals (rather than
relying on Wald approximations).

Please follow up on r-sig-mixed-models at r-project.org if you have 
further questions ...


On Mon, Jun 15, 2015 at 9:10 AM, Annika Schirmer 
<aschirme at uni-potsdam.de> wrote:

Dear Mr. Bolker, we experienced some discrepancies/problems
with the lme4 package in R and would like to get your expertise
on the subject. We?ve run mixed models with the functions lmer
and glmer and as of late the outputs given from the summary
function changed. The same models that were run a couple of 
month ago produce now a different output, specifically
different standard errors, t values/ z values and p values and
now scaled residuals are stated. Changes in the specifications
of the models were not made. In most cases those differences
are minimal and do not change the overall results of the 
models, but in some extreme cases the standard errors
experienced an extreme change that led them to become very
small and of the same value for all the fixed factors in the
model. As a result these models now produce highly significant
p values which was not the case a few month ago. Therefore the
new results seem to us highly unlikely and untrustworthy. The
same discrepancy in the models happens on two independent
computers, therefore we exclude a general software problem. The
R and lme4 versions we`re both working with are:

R version 3.2.0 (2015-04-16) Platform: x86_64-w64-mingw32/x64
(64-bit) Running under: Windows 8 x64 (build 9200

locale: [1] LC_COLLATE=German_Germany.1252
LC_CTYPE=German_Germany.1252 [3]
LC_MONETARY=German_Germany.1252 LC_NUMERIC=C [5]
LC_TIME=German_Germany.1252

attached base packages: [1] stats  graphics  grDevices utils
datasets  methods  base

other attached packages: [1] lme4_1.1-7  Rcpp_0.11.6
Matrix_1.2-0

loaded via a namespace (and not attached): [1] minqa_1.2.4
MASS_7.3-40  splines_3.2.0  nlme_3.1-120 [5] grid_3.2.0
nloptr_1.0.4  lattice_0.20-31

Was there a recent change in the lme4 package that could have
lead to the new output? Or could there maybe be a compatibility
problem between the newest version of R and lme4?

We couldn`t find anything about similar problems on the
internet thats why we turn directly to you, in case it might be
a general problem that needs fixing or you have any idea what
might be the problem.

We would be very thankful for any kind of tip you could give
us. Thanks in advance. Kind regards,

Julia Hoffmann & Annika Schirmer