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
Distinguished Professor
Founding Faculty
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William R. Shadish
University of California
School of Social Sciences, Humanities and Arts
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ATTN: William Shadish
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209-228-4372 voice
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