effects packages for mixed model?

Dear John,

I've wanted to extend the effects package to mixed-effects models for some
time now. The basics are quite simple and you should be able to do the
computations yourself using the estimated fixed effects and their covariance
matrix. 

The tricky computations are for models that have data-dependent bases, such
as those including regression spline or orthogonal polynomial terms. In the
limited time I've had to look at the problem, I haven't figured out how to
get so-called safe predictions for mixed models. Simply using predict()
isn't sufficient, since the effect() function has to manipulate the model
matrix directly.

Regards,
 John

--------------------------------
John Fox
Senator William McMaster
  Professor of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of array chip
Sent: January-17-11 1:08 AM
To: r-help at r-project.org
Subject: [R] effects packages for mixed model?

Hi, I am wondering if there is a similar effects package for mixed
models, just like what effects package does for linear, generalized
linear models?
Specifically I am looking for a way to calculate the SAS-co-called least
squared means (LS means) in mixed models (I understand there is a
substantial debate on whether such adjusted means should be computed in
the first place).

Thank you,

John



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