[R] package mgcv - predict with bam: Error in X[ind, ] :, subscript out of bounds

Gavin Simpson ucfagls at gmail.com
Tue Feb 4 00:33:05 CET 2014


The two distributions are different. The random effect is assumed to
be a Gaussian random variable, just as it is with the GLMMs in the
lme4 package. It is fine to use such a random effect within a GAM with
a non-Gaussian error distribution, like the ones you describe using.

HTH

Gavin

On 3 February 2014 15:00, William Shadish <wshadish at ucmerced.edu> wrote:
> 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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-- 
Gavin Simpson, PhD




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