package mgcv - predict with bam: Error in X[ind, ] :, subscript out of bounds
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 Distinguished Professor Founding Faculty Mailing Address: William R. Shadish University of California School of Social Sciences, Humanities and Arts 5200 North Lake Rd Merced CA 95343 Physical/Delivery Address: University of California Merced ATTN: William Shadish School of Social Sciences, Humanities and Arts Facilities Services Building A 5200 North Lake Rd. Merced, CA 95343 209-228-4372 voice 209-228-4007 fax (communal fax: be sure to include cover sheet) wshadish at ucmerced.edu http://faculty.ucmerced.edu/wshadish/index.htm http://psychology.ucmerced.edu
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Gavin Simpson, PhD