[R-sig-ME] Interaction between random and fixed effects
th|erry@onke||nx @end|ng |rom |nbo@be
Mon May 31 21:52:47 CEST 2021
I think the problem is with your response variable. It seems like you have
a lot of observations with a response value a few orders of magnitude
smaller than the global average. This grouping is not explained by any of
the covariates in your model, leading to huge random effect BLUPs.
Splitting the BLUPs over two variables probably yields a smaller penalty.
Fitting the model with a log transformed response leads to a singular model
with 0 variance for Local. This strengthens my belief that the problem is
with the data.
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
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
Op ma 31 mei 2021 om 21:21 schreef Vinicius Maia <
vinicius.a.maia77 using gmail.com>:
> Dear Thierry,
> Thank you for your response.
> Local is coded with the name of the local, but I believe the nesting is
> implicit in the data.
> with(dataset, isNested(as.character(Local), as.character(Year)))
> returns TRUE
> The example is attached.
> Best wishes,
> Em seg., 31 de mai. de 2021 às 16:10, Thierry Onkelinx <
> thierry.onkelinx using inbo.be> escreveu:
>> 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 using inbo.be
>> Havenlaan 88 bus 73, 1000 Brussel
>> 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
>> Op ma 31 mei 2021 om 20:57 schreef Vinicius Maia <
>> vinicius.a.maia77 using gmail.com>:
>>> Hi all,
>>> I have a subtle doubt in how to interpret the interaction between fixed
>>> 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) +
>>> 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
>>> why the model ran and has a high variance for the interaction, but if it
>>> the later case (which I believe it is), how does the model estimate an
>>> interaction for Local:Year if they are nested?
>>> Best wishes,
>>> Vinícius Maia
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>>> R-sig-mixed-models using r-project.org mailing list
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