Thanks a lot, Douglas. It's very heplful. A clarification question about specifying the model in lmer. You said it should be: mix.lmer <- lmer(DV ~a+b+c+d+(e+f+g+h+ii|group), mydata)
I assume it was a typo and you meant that the last predictor in brackets should be i (rather than ii), right? Also you said: "I wouldn't recommend it though as this requires estimating 21 variance and covariance parameters for the random effects. Almost certainly the estimated variance-covariance matrix will end up being singular. Unless you are careful you may not notice this." Question: What would you recommend one to do in the following situation: I have quite a few predictors that are all fixed (and are usually estimated using simple OLS regression). But now I have the same situation, however, my observations come from different groupings (the same number of observations per grouping, the same set of predictors and the same DV in all groupings). I thought I would use the factor "group" to define the random effects model - because I didn't want predictor coefficients to vary like crazy from group to group - I wanted them to be "anchored" in the pooled model's predictor coefficients. But if it's not feasible mathematically - what should one do? Maybe run a bunch of models like this: mix.lmer.e <- lmer(DV ~a+b+c+d+ (e|group)+ f+g+h+i, mydata) mix.lmer.f <- lmer(DV ~a+b+c+d+ e + (f|group)+ g+h+i, mydata) mix.lmer.g <- lmer(DV ~a+b+c+d+ e + f + (g|group) + h+i, mydata) etc. Look at the random effect for each and every predictor in question? But would it be right? My ultimate goal - is to accurately estimate the original regression model (DV ~a+b+c+d+e+f+g+h+i) - but for each of the different groups... Thanks a lot for your advice! Dimitri On Tue, Oct 26, 2010 at 3:57 PM, Douglas Bates <ba...@stat.wisc.edu> wrote: > On Tue, Oct 26, 2010 at 12:27 PM, Dimitri Liakhovitski > <dimitri.liakhovit...@gmail.com> wrote: >> Hello, >> and sorry for asking a question without the data - hope it can still >> be answered: > >> I've run two things on the same data: > >> # Using lme: >> mix.lme <- lme(DV ~a+b+c+d+e+f+h+i, random = random = ~ e+f+h+i| >> group, data = mydata) > >> # Using lmer >> mix.lmer <- lmer(DV >> ~a+b+c+d+(1|group)+(e|group)+(f|group)+(h|group)+(i|group), data = >> mydata) > > Those models aren't the same and the model for lmer doesn't make > sense. You would need to write the random effects terms as > (0+e|group), etc. because (e|group) is the same as (1 + e|group) so > you are including (Intercept) random effects for group in each of > those 5 terms. > > To generate the same model as you fit with lme you would use > > mix.lmer <- lmer(DV ~a+b+c+d+(e+f+g+h+ii|group), mydata) > > I wouldn't recommend it though as this requires estimating 21 > variance and covariance parameters for the random effects. Almost > certainly the estimated variance-covariance matrix will end up being > singular. Unless you are careful you may not notice this. > >> lme provided an output (fixed effects and random effects coefficients). > > lme is not as picky about singularity of the variance-covariance > matrix as lmer is. > >> lmer gave me an error: Error in mer_finalize(ans) : Downdated X'X is >> not positive definite, 10. >> I've rerun lmer with - but specifying the random effects for 2 fewer >> predictors. This time it ran and provided an output. (BTW, the random >> effects of lmer with 2 fewer predictors specified as random were very >> close to the output of lme). > > Yes, lmer could converge in such as case but the parameter estimates > are not meaningful because of the ambiguity described above. > >> Question: >> Looks like lmer could not invert the matrix, right? > > Well, lmer never tries to invert matrices but it does factor them and > that is where the problem is recognized. However, I think that > singularity is a symptom of the problem, not the cause. > >> But how come lme >> (which I thought was an earlier version of lmer) COULD invert it? > > The computational methods in the two packages are quite different. I > think that the methods in lme4 are superior because we have learned a > bit in the last 10 years. > > >> Greatly appreciate a clarification! >> >> >> -- >> 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. >> > -- 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.
