Dear Rudi,
Please keep the mailing list in cc.
Yes. I'd use the model with only the 3-way interaction smoother. It
can handle all patterns of the lower interactions.
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 <mailto:thierry.onkelinx at inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be <http://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
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<https://www.inbo.be>
Op di 25 mei 2021 om 13:30 schreef Rudi Reiner <rudi.reiner at boku.ac.at
<mailto:rudi.reiner at boku.ac.at>>:
Dear Thierry,
thank you for your reply. I am afraid, there was a typing error in
my initial message. Age and b.mass do have a non-linear (not lon
or log linear) relationship. Simplified, it may be a quadratic one
(see me model using lmer) but I think a gam would fit better. So
would you still suggest this model?:
gam(b.mass ~ te(age, area.forest, by = period), data = data,
random = list(pop=~1, year=~1))
Best regards,
Rudi
Am 25.05.2021 um 13:04 schrieb 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 at inbo.be <mailto:thierry.onkelinx at inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be <http://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 di 25 mei 2021 om 09:14 schreef Rudi Reiner
<rudi.reiner at boku.ac.at <mailto:rudi.reiner at 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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