Interaction between random and fixed effects
Dear Vinicius, What did you ran the interaction as a fixed effect Year:Local or a random effect (1|Year:Local)? How did you code Local: as a unique value for every Local and Year combinations? Please do share output or a minimal example so we know exactly what you did. I'm still a novice at mind reading. 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> Op ma 31 mei 2021 om 20:57 schreef Vinicius Maia < vinicius.a.maia77 at gmail.com>:
Hi all,
I have a subtle doubt in how to interpret the interaction between fixed (or
even random) and random effects in the following case.
I have a model: Y ~ Year + (1|Local)+(1|Genotype) + (Year:Local:Genotype)
Year is a fixed effect because it has only 4 levels.
I ran the model with the random and fixed effect interaction just to
explore, but I was not expecting that it would work because Locals are
completely nested within Years.
To my surprise, the model ran and the variance of Year:Local:Genotype are
quite big. How is it possible to have Local interacting with Year if they
are nested? I also tried: Y ~ Year + (1|Local)+(1|Genotype) + (Year:Local)
and the model rans too, without singular fit.
I am struggling to understand if random interactions (it also extends to
cases where the interactions are only between nested random effects) mean
that the variance between Locals changes with Years or if the effect of a
given Local changes with Years. If it is the former option I can understand
why the model ran and has a high variance for the interaction, but if it is
the later case (which I believe it is), how does the model estimate an
interaction for Local:Year if they are nested?
Thanks!
Best wishes,
Vin?cius Maia
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