From: Spencer Graves <spencer.graves_at_pdf.com>

Date: Tue 27 Jun 2006 - 01:51:20 EST

*>> 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
*

*>>
*

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jun 27 01:55:49 2006

Date: Tue 27 Jun 2006 - 01:51:20 EST

<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:

> 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. > ______________________________________________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 Received on Tue Jun 27 01:55:49 2006

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