[R-sig-ME] Large variance and SD for random effects

[Thierry Onkelinx]

Dear Paul,

Yes, I see no point in keeping an unstable model. Remember "All models are
wrong but some are useful" (Box, 1979). I'd investigate where the complete
separation occurs and decide which random slope should be removed.

In case of the strong correlations among random effects see
https://www.muscardinus.be/2018/02/highly-correlated-random-effects/

Best regards,


ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
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<https://www.inbo.be>

2018-03-06 11:02 GMT+01:00 cumuluss <cumuluss at gmx.de>:

> Dear Thierry,
> sorry one more question. i would like to ask whether you could give me
> some recommendation for my model. Would you skip random effects (or
> slopes) even if you think they are necessary, as far as they lead to
> suffering models? It is maybe a more general question.
> Thank you!
>
>
>
>
> Dear Thierry,
> thank you for your answer!
> Ok then I have to rethink the model.
> Best regards
> Paul
>
>
>
> Thierry Onkelinx:
> > Dear Paul,
> >
> > Your random effect structure looks quite complicated. Maybe too complex
> for
> > the data. Your model is very likely suffering from (quasi) complete
> > separation.
> >
> > Besides the large variances, you should also be alarmed by the near
> perfect
> > correlations among some random effects.
> >
> > Best regards,
> >
> >
> >
> > ir. Thierry Onkelinx
> > Statisticus / Statistician
> >
> > Vlaamse Overheid / Government of Flanders
> > INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
> AND
> > FOREST
> > Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> > thierry.onkelinx at inbo.be
> > Havenlaan 88 bus 73, 1000 Brussel
> > www.inbo.be
> >
> > ////////////////////////////////////////////////////////////
> ///////////////////////////////
> > To call in the statistician after the experiment is done may be no more
> > than asking him to perform a post-mortem examination: he may be able to
> say
> > what the experiment died of. ~ Sir Ronald Aylmer Fisher
> > The plural of anecdote is not data. ~ Roger Brinner
> > The combination of some data and an aching desire for an answer does not
> > ensure that a reasonable answer can be extracted from a given body of
> data.
> > ~ John Tukey
> > ////////////////////////////////////////////////////////////
> ///////////////////////////////
> >
> > <https://www.inbo.be>
> >
> > 2018-03-05 20:24 GMT+01:00 cumuluss <cumuluss at gmx.de>:
> >
> >> Hello,
> >> the result of my GLMM with binomial error structure revealed for one of
> >> the random intercepts and slopes variances and Sd's larger then 1
> >>
> >>> Generalized linear mixed model fit by maximum likelihood (Laplace
> >> Approximation) ['glmerMod']
> >>>  Family: binomial  ( logit )
> >>> Formula: obs.yn ~ z.lengthxyz + z.obsX + Spe_tr_subspecie + (1 |
> commu) +
> >>>     (1 + z.lengthxyz + z.obsX | Siteun) + (1 + z.lengthxyz +
> >>>     z.obsX + Spe_tr_subspecie_a.c + Spe_tr_subspecie_b.c +
> >>>     Spe_tr_subspecie_c.c | behavior)
> >>
> >>> Random effects:
> >>>  Groups   Name                  Variance Std.Dev. Corr
> >>>  commu    (Intercept)           0.614004 0.78358
> >>>  Siteun   (Intercept)           0.521198 0.72194
> >>>           z.lengthxyz           0.001016 0.03188   1.00
> >>>           z.obsX                0.306931 0.55401  -0.17 -0.19
> >>>  behavior (Intercept)           2.966139 1.72225
> >>>           z.lengthxyz           0.030171 0.17370   0.77
> >>>           z.obsX                0.412169 0.64200   0.20 -0.36
> >>>           Spe_tr_subspecie_a.c  1.853903 1.36158   0.58  0.62  0.11
> >>>           Spe_tr_subspecie_b.c  3.973439 1.99335   0.51  0.23  0.25
> 0.65
> >>>           Spe_tr_subspecie_c.c  7.401343 2.72054   0.39  0.30  0.47
> >> 0.79  0.60
> >>> Number of obs: 4413, groups:  commu, 144; Siteun, 108; behavior, 31
> >>
> >> Now I wonder, whether that is a reason to worry, that the result could
> >> be not valid?
> >> Thanks in advance for any comments!
> >> Paul
> >>
> >> _______________________________________________
> >> R-sig-mixed-models at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>
> >
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> >
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> >
>

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