<see inline> Rick Bilonick wrote: > On Fri, 2006-06-23 at 21:38 -0700, Spencer Graves wrote: >> Permit me to try to repeat what I said earlier a little more clearly: >> When the outcomes are constant for each subject, either all 0's or all >> 1's, the maximum likelihood estimate of the between-subject variance in >> Inf. Any software that returns a different answer is wrong. This is NOT >> a criticism of 'lmer' or SAS NLMIXED: This is a sufficiently rare, >> extreme case that the software does not test for it and doesn't handle >> it well when it occurs. Adding other explanatory variables to the model >> only makes this problem worse, because anything that will produce >> complete separation for each subject will produce this kind of >> instability. >> >> Consider the following: >> >> library(lme4) >> DF <- data.frame(y=c(0,0, 0,1, 1,1), >> Subj=rep(letters[1:3], each=2), >> x=rep(c(-1, 1), 3)) >> fit1 <- lmer(y~1+(1|Subj), data=DF, family=binomial) >> >> # 'lmer' works fine here, because the outcomes from >> # 1 of the 3 subjects is not constant. >> >> > fit.x <- lmer(y~x+(1|Subj), data=DF, family=binomial) >> Warning message: >> IRLS iterations for PQL did not converge >> >> The addition of 'x' to the model now allows complete separation for >> each subject. We see this in the result: >> >> Generalized linear mixed model fit using PQL >> <snip> >> Random effects: >> Groups Name Variance Std.Dev. >> Subj (Intercept) 3.5357e+20 1.8803e+10 >> number of obs: 6, groups: Subj, 3 >> >> Estimated scale (compare to 1) 9.9414e-09 >> >> Fixed effects: >> Estimate Std. Error z value Pr(>|z|) >> (Intercept) -5.4172e-05 1.0856e+10 -4.99e-15 1 >> x 8.6474e+01 2.7397e+07 3.1563e-06 1 >> >> Note that the subject variance is 3.5e20, the estimate for x is 86 >> wit a standard error of 2.7e7. All three of these numbers are reaching >> for Inf; lmer quit before it got there. >> >> Does this make any sense, or are we still misunderstanding one >> another? >> >> Hope this helps. >> Spencer Graves >> > Yes, thanks, it's clear. I had created a new data set that has each > subject with just one observation and randomly sampled one observation > from each subject with two observations (they are right and left eyes). > I'm not sure why lmer gives small estimated variances for the random > effects when it should be infinite.
SG: If lmer gave me small estimated variances for the random effects, I would check very carefully my model, as I would believe I probably have specified something incorrectly. I ran NLMIXED on the original data > set with several explanatory factors and the variance component was in > the thousands. > > I guess the moral is before you do any computations you have to make > sure the procedure makes sense for the data. > > Rick B. > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
