[R-sig-ME] GAMMs: difference smooths in itsadug

[Sebastian Sauppe]

Dear Cesko,

Thanks a lot, that was exactly the info I was looking for!

Regards,
Sebastian

-----------
Dr. Sebastian Sauppe
Department of Comparative Linguistics, University of Zurich
Homepage: https://sites.google.com/site/sauppes/ <https://sites.google.com/site/sauppes/>
Twitter: @SebastianSauppe <https://twitter.com/SebastianSauppe>
Google Scholar Citations: https://scholar.google.de/citations?user=wEtciKQAAAAJ <https://scholar.google.de/citations?user=wEtciKQAAAAJ> 
ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe <http://www.researchgate.net/profile/Sebastian_Sauppe>
ORCID ID: http://orcid.org/0000-0001-8670-8197 <http://orcid.org/0000-0001-8670-8197>

> Am 16.07.2018 um 15:26 schrieb Voeten, C.C. <c.c.voeten using hum.leidenuniv.nl>:
> 
> Hi Sebastian,
> 
> Yes, rm.ranef=TRUE will give you precisely this, assuming your random effects are all of the 'fs' and/or 're' category, which is the case for the model you describe.
> With rm.ranef=FALSE, you would get the effects specifically for the first subject+item combination in your data set (or, more precisely, whichever of these happened to be the first level in your factor variables for these terms).
> 
> Best,
> Cesko
> 
>> -----Oorspronkelijk bericht-----
>> Van: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces using r-
>> project.org] Namens Sebastian Sauppe
>> Verzonden: maandag 16 juli 2018 9:20
>> Aan: r-sig-mixed-models using r-project.org
>> Onderwerp: [R-sig-ME] GAMMs: difference smooths in itsadug
>> 
>> Dear list members,
>> 
>> I have a question on the treatment of random effects in plotting difference
>> smooths for GAMMs with the package itsadug.
>> 
>> I am modelling the time course of binomial data with mgcv::bam. The
>> simplified formula is: cbind(success, failure) ~ 1 + s(Time, by = Condition) +
>> s(Subject, Time, bs = „fs“) + s(Item, Time, bs = „fs“). The two factor smooths
>> are supposed to account for the random effects of participants and stimuli in
>> my experiments.
>> 
>> I would like to use itsadug::plot_diff() to visualize how the two conditions
>> differ over time. However, I am not quite sure what the rm.ranef argument
>> argument of this function does. What I basically want to do is to look at the
>> difference the way one would look at a fixed effect in an GLMER model, i.e.,
>> looking at the fixed effect of the interaction of Time:Condition after the
>> variance that can be ascribed to random effects of subjects and items have
>> been accounted for. Would for this rm.ranef=TRUE or rm.ranef=FALSE be the
>> right option?
>> 
>> Kind regards,
>> Sebastian
>> 
>> -----------
>> Dr. Sebastian Sauppe
>> Department of Comparative Linguistics, University of Zurich
>> Homepage: https://sites.google.com/site/sauppes/
>> <https://sites.google.com/site/sauppes/>
>> Twitter: @SebastianSauppe <https://twitter.com/SebastianSauppe>
>> Google Scholar Citations:
>> https://scholar.google.de/citations?user=wEtciKQAAAAJ
>> <https://scholar.google.de/citations?user=wEtciKQAAAAJ>
>> ResearchGate: http://www.researchgate.net/profile/Sebastian_Sauppe
>> <http://www.researchgate.net/profile/Sebastian_Sauppe>
>> ORCID ID: http://orcid.org/0000-0001-8670-8197 <http://orcid.org/0000-0001-
>> 8670-8197>
>> 
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>> 
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