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-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > On Behalf Of array chip > Sent: January-17-11 1:08 AM > To: r-help@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 > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
