From: Jim Lindsey <jlindsey@luc.ac.be> Message-Id: <9802251048.AA06692@alpha.luc.ac.be> Subject: Re: R-beta: repeated measures To: p.dalgaard@biostat.ku.dk (Peter Dalgaard BSA) Date: Wed, 25 Feb 1998 11:48:07 +0100 (MET) In-Reply-To: <x2ogzw7z2t.fsf@blueberry.kubism.ku.dk> from "Peter Dalgaard BSA" at Feb 25, 98 11:34:18 am > > Jim Lindsey <jlindsey@luc.ac.be> writes: > > > > Jim> R itself has nothing for repeated measures. However, I am developing a > > > Jim> complete set of four libraries that will handle most any repeated > > > Jim> measures problem, normally distributed or other. This includes two > > > Jim> functions in one of the libraries that will do the so-called anova > > > Jim> approach plus ARMA. They will do everything in my Repeated > > > Jim> Measurements book (OUP 1993) and much more. > > > Jim> I am waiting until R stabilizes a bit before releasing them but will > > > Jim> soon be asking for volunteers to aid in preliminary testing. > > > Jim> Jim > > > > ... > > > > > > He was also badly missing S-plus's aov(.) function... > > > and I explained how he could use anova( lm(...) ) for FIXED effects > > > anova, and that nothing is yet available for random (or mixed) effects. > > > > > > Martin. > > > > > Just a warning. These things are not as simple as aov. It should still > > be implemented! Mine are general enough to do any nonlinear model for > > both mean and variance (e.g.PKPD), generalized linear mixed models, > > multivariate survival with any censoring pattern, completely > > unbalanced data, etc. Hence, with this level of generality, the > > interface is not always that simple: lists of time-varying covariates etc. > > Jim > > Yes. We need aov(). > > Also note that Bates & Pinheiro are in the process of porting lme > (linear mixed effects models) to R. From what I have seen, this does > at least what SAS PROC MIXED does, only better... My libraries also do all of SAS Proc Mixed. One difference with respect to nlme is that I have not implemented models with more than one random effect, and certainly not for nonlinear models i.e just random intercept with nonlinear models, but any distribution, not just normal. I am waiting impatiently for that aspect of nlme. Jim -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request@stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._