There is a very nice paper that illustrates a flexible approach that allows
the user to specify nearly any possible prediction he or she may desire.  A
flexible system is critical since "one man's fixed effect is another man's
random effect" to quote Schabenberger.

@article{welham2004prediction,
  title={{Prediction in linear mixed models}},
  author={Welham, S. and Cullis, B. and Gogel, B. and Gilmour, A. and
Thompson, R.},
  journal={Australian \& New Zealand Journal of Statistics},
  volume={46},
  number={3},
  pages={325--347},
  year={2004},
  publisher={John Wiley \& Sons}
}

Kevin Wright

On Tue, Mar 23, 2010 at 8:15 PM, Ben Bolker <bolker@ufl.edu> wrote:

> Douglas Bates <bates <at> stat.wisc.edu> writes:
>
> > It is not easy to decide what "predict" should return for a linear
> > mixed model, let alone the more complicated cases.  Do you want
> > predictions based on the fixed-effects only or based on a combination
> > of the fixed-effects and the random-effects?  For the lme function in
> > the nlme package we allowed "levels" of predictions but that won't
> > work for all models that can be fit with lme4.
>
>   I do actually think there's a reasonable way to specify this --
> an indicator specifying which random effects should have their
> conditional modes included in the prediction (the default could
> be either 'none' or 'all').
>
>  Further discussion should presumably move to r-sig-mixed-models.
> >
> > On Tue, Mar 23, 2010 at 9:18 AM, Markus Loecher wrote:
> > > Dear mixed effects modelers,
> > > I seem unable to find a predict method for mer objects in the package
> lme4.
> > > Am I not seeing the forest for the trees ?
>
>   In the meantime you can construct your own model matrices
> and use them to generate the predictions by hand -- see
>
> http://glmm.wikidot.com/local--files/examples/Owls.pdf
> http://glmm.wikidot.com/local--files/examples/Owls.Rnw
>
>  for a bit of a worked example.
>
>  Ben Bolker
>
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