Dear Simon, your note below says "bs="re" specifies a Gaussian random effect
". I have been using bs = "re" for data modeled with Poisson and binomial
distributions, or variants thereof (e.g., quasi-Poisson). Have I erred in
assuming bs ="re" can be used to obtain random effects for such data? Will
Shadish
- Actually this is ok. mgcv exploits the duality between quadratically
penalized smooths and Gaussian random effects to allow random effects to
be specified this way. bs="re" specifies a Gaussian random effect with
corresponding model matrix given by model.matrix(~site-1). (More
generally s(x,y,z,bs="re") specifies a gaussian random effect with model
matrix given by model.matrix(~x:y:z-1), with obvious generalization to
more or fewer variables). See mgcv help file ?random.effects for more.
best,
Simon
--
William R. Shadish
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