[R-sig-ME] Generalized mixed models for poisson distributions

Page E. Van Meter vanmete7 at msu.edu
Fri Oct 24 22:41:24 CEST 2008

To clarify the simple mistake I was making for possible future novice 
readers of this mailing list:
I did not understand that the glm function cannot handle random effects 
(which Faraway NEVER mentions in his book). I also did not understand 
that glmer and lmer can both handle non-linear data as long as the 
"family" is specified.

I am very comfortable with linear models, linear mixed effect models, 
and non-linear models, but I have been having a lot of trouble tackling 
non-linear mixed effect models. I really do wish we could all agree on 
terms for these models (GLM is used for both general linear mixed models 
and generalized mixed models in many fields). Anyway, these were very 
rudimentary stumbling blocks that were not immediately apparent to me, 
so hopefully this will clarify for others.
Thanks again,

Ben Bolker wrote:
> glm doesn't do mixed effects models at all.
>   You might (?) be confused about this because some software
> packages (SAS in particular, I don't know about SPSS) use the acronym
> GLM to refer to "general linear models" (rather than general*ized*
> linear models).  Some particular kinds of linear mixed models
> (nested, balanced designs) can be estimated using the same
> general approaches used for ANOVA (in R, this would correspond
> to using aov with an Error term in the model).
>   Linear models: lm (or aov)
>   Generalized linear models: glm
>   For linear mixed models you need lme (in the nlme package)
> or lmer (lme4).  For generalized linear mixed models you
> need glmmPQL (MASS/nlme) or glmer (lme4).
>   Ben Bolker

Page E. Van Meter
Michigan State University
Department of Zoology
vanmete7 at msu.edu

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