[R-sig-ME] Interaction between random and fixed effects

[Vinicius Maia]

Thanks!

Best regards,

Vinícius

Em ter., 1 de jun. de 2021 às 03:44, Thierry Onkelinx <
thierry.onkelinx using inbo.be> escreveu:

> No. It doesn't make sense to use an interaction term. Year + (1|Local) is
> sufficient in this case.
>
> 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
>
>
> ///////////////////////////////////////////////////////////////////////////////////////////
> 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 22:02 schreef Vinicius Maia <
> vinicius.a.maia77 using gmail.com>:
>
>> Thank you a lot Thierry,
>>
>> But besides that, is it sensical to estimate an interaction such as
>> local:year if local is nested within year? I believe it does not make sense
>> but when this model ran I started to question what I know.
>>
>> Best wishes,
>>
>> Vinícius
>>
>>
>>
>> Em seg., 31 de mai. de 2021 às 16:52, Thierry Onkelinx <
>> thierry.onkelinx using inbo.be> escreveu:
>>
>>> Dear Vinicius,
>>>
>>> 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.
>>>
>>> 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
>>>
>>>
>>> ///////////////////////////////////////////////////////////////////////////////////////////
>>> 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 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,
>>>>
>>>> Vinícius
>>>>
>>>> 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
>>>>> 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 using 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,
>>>>>>
>>>>>> Vinícius Maia
>>>>>>
>>>>>>         [[alternative HTML version deleted]]
>>>>>>
>>>>>> _______________________________________________
>>>>>> R-sig-mixed-models using r-project.org mailing list
>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>>
>>>>>

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