[R-sig-ME] discrepancies in lme4

[Julia Hoffmann]

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