[R-sig-ME] 3-way interaction in random structure using gam

[Thierry Onkelinx]

Dear Rudi,

If age has a log-linear relationship, then use logAge as predictor rather
than age.

I'm wondering why you insist on adding the 2-way interactions smoothers.
You can't directly interpret 2-way interactions (or main effects) when you
have a 3-way interaction that contains the same variables.

I'd simplify the model to  gam(b.mass ~ te(log.age, area.forest, by =
period), data = data, random = list(pop=~1, year=~1)).

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
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www.inbo.be

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Op di 25 mei 2021 om 09:14 schreef Rudi Reiner <rudi.reiner using boku.ac.at>:

>
> Hi there,
>
> my actually trying to fit a non-linear mixed model (with a random
> structure) and am not sure how to do this correct using "gam" or "bam"
> from the mgcv package. My supervisor suggested me to ask R-sig.
>
> My Data:
>
> b.mass = body mass (continuous) - dependent variable
> _
> __predictor variables_
> age = continuous; lon-linear relationship with b.mass
> area.forest = continuous
> period =  factor; 2 levels
>
> _random variables
> _year = continuous (1993-2019)
> pop = factor (28 different populations)
> --------------------------------------------------
>
> First I fitted a linear model with quadratic age which seems to give me
> the "correct" results (biologically the results make sense). Especially
> I am interested in the 3-way interaction age x area.forest x period but
> also want/have to add all 2-way interactions:
>
> */LM <- lmer(b.mass ~ ns(age,2)*area.forest*period + (1|pop)+ (1|year),
> data = data)/*
>
> Now I want to have the corresponding model using gam (or bam). I tried:
>
> */gam <- gam(b.mass ~ period+s(age)+area.forest+s(age, by =
> area.forest)+s(age, by=period)+te(area.forest, by=period)+te(age,
> area.forest, by = period), data = data, random = list(pop=~1, year=~1))/*
>
> The results (plot age~b.mass for different area.forest) look different
> to the lmer approach. Do have an idea, if my model (gam) is fitted
> correct, i.e., is it the "same" like the model I fitted using /`lmer`?/
>
> Thank you,
> Rudi
>
>
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