Yes, sorry for the confusion. Maybe I should have used a different term. So, I guess, I was right - it gives only the random effects that I have to add to the fixed effects. And there is no way to get it done by R (not that I can't do it myself)? Dimitri
On Mon, Oct 18, 2010 at 6:24 PM, Bert Gunter <gunter.ber...@gene.com> wrote: > Oh -- I get your question (I think). Not the total, just the random > effects. You have to add them to the fixed effects. > > See e.g. p. 39 of Bates and Pinheiro. > > -- Bert > > On Mon, Oct 18, 2010 at 3:00 PM, Dimitri Liakhovitski > <dimitri.liakhovit...@gmail.com> wrote: >> Thank you very much, but not I am not sure now - does ranef(fm1) give >> the (total) slope and >> intercept values directly for each group or not? >> Thanks a lot for clarifying - because I might well have been wrong. >> Dimitri >> >> On Mon, Oct 18, 2010 at 5:57 PM, Bert Gunter <gunter.ber...@gene.com> wrote: >>> Dmitri: >>> >>> Not quite sure what you mean by easier ... fixef() and ranef() will >>> both give coefficients which can be easily manipulated to produce the >>> results for all subjects. >>> >>> However, note that there are numerous built-in lme >>> functions(especially for graphics) that do this internally to produce, >>> e.g. graphs of coefficient shrinkage. So if this is the sort of thing >>> you want to do with the BLUPS, you may not need to do it manually. >>> >>> HTH. >>> >>> Cheers, >>> Bert >>> >>> On Mon, Oct 18, 2010 at 2:15 PM, Dimitri Liakhovitski >>> <dimitri.liakhovit...@gmail.com> wrote: >>>> Hello! >>>> >>>> If I run this example: >>>> >>>> library(nlme) >>>> fm1 <- lme(distance ~ age+Sex, Orthodont, random = ~ age + Sex| Subject) >>>> If I run: >>>> summary(fm1) >>>> then I can see the fixed effects for age and sex (17.7 for intercept, >>>> 0.66 for age, and -1.66 for SexFemale) >>>> >>>> If I run: >>>> ranef(fm1) >>>> Then it looks like it's producing the random effects for each subgroup >>>> (in this example - each subject). For example, for MO1 it's: >>>> 1.25 for intercept, 0.106 for age, and -1.52 for SexFemale. >>>> >>>> So, in order to get the the total effects, i.e., the regression >>>> equation, for each subgroup (Subject) I need to do this: >>>> For example, for Subject MO1: >>>> y(M01) = (17.71+1.25)+(0.66+0.106)*Age+(-1.66-1.52)*SexFemale = 18.96 >>>> + 0.766*Age -3.18*SexFemale >>>> >>>> Question: Is there an easier way to get such an equation for each >>>> level of Subject? >>>> >>>> Thank you very much! >>>> >>>> -- >>>> Dimitri Liakhovitski >>>> Ninah Consulting >>>> www.ninah.com >>>> >>>> ______________________________________________ >>>> 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. >>>> >>> >>> >>> >>> -- >>> Bert Gunter >>> Genentech Nonclinical Biostatistics >>> >> >> >> >> -- >> Dimitri Liakhovitski >> Ninah Consulting >> www.ninah.com >> > > > > -- > Bert Gunter > Genentech Nonclinical Biostatistics > -- Dimitri Liakhovitski Ninah Consulting www.ninah.com ______________________________________________ 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.
