It's always a bit risky to reach in and grab pieces of the internal
structure of objects created in a package because the internal structure
can change.  I recently dismayed my co-authors by telling them that in
reading about the sparse matrix capabilities in CUDA I found that there is
a better representation of mixed-models than the one we are using.  They
don't want to hear about it.

Having said that, the idea of the predictor modules (which is what the pp
slot is) is that they provide the linear predictor for given parameter
values and they can update the parameter increment according to updated
weighted residuals and updated 'X' weights.  In general the linear
predictor is determined by a step factor as beta0 + k * delb for the fixed
effects and u0 + k * delu for the orthogonalized random effects.  In the
case of a linear mixed effects model the beta0 and u0 fields of a merPredD
object are always 0 during the iterations.  I forget whether they are
rewritten on completion of the iterations.


On Wed, Nov 7, 2012 at 10:27 AM, Ulrich Halekoh <Ulrich.Halekoh@agrsci.dk>wrote:

> Dear,
> I would like to know the meaning of the beta0 of a fitted lmer-object
>  using
> the lme4 package from R-Forge version '0.99999911.0'
>
> Example:
> data(Orthodont,package='nlme')
> library(lme4) #R-forge!
> mot<-lmer(distance ~ Sex + (1|Subject),data=Orthodont)
>
> mot@pp$beta0
>
>
> Kind regards
>
> Ulrich Halekoh
> Associate Professor
> Email: Ulrich.Halekoh@agrsci.dk
> Aarhus University; Denmark
>
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> R-sig-mixed-models@r-project.org mailing list
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>

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