In case you haven't already received an adequate reply (which I haven't seen) or figured this out on your own, I will comment. Consider the following modifications of an example in the 'lmer' documentation:
(fm0.0 <- lmer(Reaction~(1|Subject), sleepstudy)) (fm0.1 <- lmer(Reaction~1+(1|Subject), sleepstudy)) (fm0.s <- lmer(Reaction~Subject+(1|Subject), sleepstudy)) The first two models are equivalent, as can be seen from looking at the output. In the "formula" language, something like "Reaction~X" means to estimate an intercept plus an X effect. If you want a no-constant model, you must specify "Reaction ~ -1+X". When X is a factor, "Reaction ~ -1+X" effectively fits the same model as "Reaction~X" using an alternative parameterization. If X is numeric, "Reaction~X" means estimate b0 and b1 in Reaction = b0 + b1*X + error. Meanwhile, "Reaction ~ -1+X" means estimate only b1 in Reaction = b1*X + error. In this latter case, the introduction of "-1" actually changes the model. The third model "Reaction~Subject+(1+Subject) is a confusion: The "~Subject" part asks lmer to estimate a separate parameter for each Subject. The (1|Subject) term asks lmer to estimate the standard deviation for between-Subject random variability after the fixed effects are removed. Since Subject is also listed as a fixed effect in this model, the model is overparameterized: I'm not certain, but it appears to me that the software doesn't know whether to allocate subject-specific deviations from the overall mean to the fixed effects coefficients or the random effect, and it appears to do a little of both. It might be nice if 'lmer' included a check for factors appearing as both fixed and random effects. However, I believe that 'lme4' and (R more generally) is primarily a research platform for new statistical algorithm development. Most of the R Core Team work to maintain R under the GNU license primarily because it supports their research (and educational) objectives. The product therefore may not strive to be as supportive for naive users as commercial software. Hope this helps. Spencer Graves Aimin Yan wrote: > consider p as random effect with 5 levels, what is difference between these > two models? > > > p5.random.p <- lmer(Y > ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > > p5.random.p1 <- lmer(Y > ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > > thanks, > > Aimin Yan > > ______________________________________________ > R-help@stat.math.ethz.ch 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@stat.math.ethz.ch 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.