[R-sig-ME] Order of terms for random slopes
Thierry Onkelinx
thierry@onkelinx @ending from inbo@be
Thu Aug 30 09:14:36 CEST 2018
Dear Stefan,
IMHO you shouldn't use an overfitted model for didatic purposes. Teach
students that you need a sufficiently large data set depending on the
complexity of the model.
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 using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
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<https://www.inbo.be>
2018-08-29 20:51 GMT+02:00 Stefan Th. Gries <stgries using gmail.com>:
> > Thanks. This is a known issue: https://github.com/lme4/lme4/issues/449
> Ohh, ok, I had googled a bit on 'order of terms', 'random effects'
> etc. but hadn't come across this, sorry.
>
> > - it's not terribly surprising that a model with 11 parameters fitted to
> 48 observations is numerically unstable ...
> Absolutely, the example is from a workshop and was used only for
> didactic purposes, and ...
>
> > there don't seem to be any _substantive_ differences in the estimate ...
> ... yes, we only wanted to make sure there wasn't something
> superobvious but important we had missed.
>
> Thanks for the quick feedback!
>
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