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I am new to Generalized additive mixed models (GAMM) and I'm trying to model a behavioral response variable (time spent shading eggs by a nesting bird in minutes timeCS) in relation to several predictor variables: maximum temperature (maxT), species (categorical), the day of the year (jdate) and the age of the nest (ca). My data was based on repeated observations at several nests of shorebirds.

I have three random effects: nest id (nest), location (rm) and year. Nest is nested within rm and year; while rm and year are crossed.

Since I have multiple random effects, I plan to use gamm4 in R as my software package to conduct the GAMM. So far I believe the correct code to run this analysis with my data is

 gamm4 <- (timeCS~s(maxT)+ species + s(jdate) + s(ca), 
           random=~(1|year)+(1|rm)+(1|rm:nest)+(1|year:nest), 
           data=Dataset, family=gaussian(link ="identity"))

Is this correct? Should I specify smooth terms for my predictor variables? Can I run model selection based on AICc on GAMM?

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  • $\begingroup$ I have the same question - did you feel this was the correct model specification for your random effects? $\endgroup$
    – Kodiakflds
    Commented Apr 9, 2021 at 17:24
  • $\begingroup$ "Can I run model selection based on AICc on GAMM" in general, using the cross-validation error that AIC (sometimes) approximates would be better $\endgroup$
    – wzbillings
    Commented Mar 31, 2023 at 15:49
  • $\begingroup$ Were the same nests observed in different years, or was renesting in the same year observed? 1|year:nest seems like it night not be necessary - that might give a random intercept for each observation $\endgroup$
    – N Brouwer
    Commented Apr 20 at 22:28

1 Answer 1

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Specifying random effect terms in gamm4 is different to mgcv. The syntax I show is provided in this book.

Two random effect terms in gamm4 is:

random = ~(1|xr1 + 1|xr2)

If they are nested, it is:

random = ~(1|xr1/xr2)
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