[R-sig-ME] Standard errors of variances; negative variance

[Ben Bolker]

   Hi, Will,

Googling

  site:https://stat.ethz.ch/pipermail/r-sig-mixed-models/ "negative 
variance"

claims to get you 67 results (although only 11 that Google considers 
worth displaying by default),

   You can filter by date - there are only two hits since 2020:

https://www.google.com/search?q=site%3Ahttps%3A%2F%2Fstat.ethz.ch%2Fpipermail%2Fr-sig-mixed-models%2F+%22negative+variance%22&client=firefox-b-d&tbs=cdr%3A1%2Ccd_min%3A01-01-2020%2Ccd_max%3A&tbm= 


   and these both look like false positives (they include "negative" and 
"variance" but not "negative variance" ...)

   The short answer is that I am not aware of any mixed effect package 
in R that will allow you to return negative values. You can see my 
answer here: 
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2018q1/026437.html ...


   As for uncertainties in variances - the merDeriv package (dating back 
to 2017) will give you the standard errors of variances and covariances 
(although again, note that there's a theoretical issue here - the 
standard errors are often extremely poor summaries of the uncertainty or 
RE variances, the original authors of lme4 would certainly prefer that 
you use profile confidence intervals to summarize the uncertainty ...)

   That's what I know -- perhaps someone else knows about a package that 
allows for negative variances ... ??

On 2023-06-19 6:13 p.m., Will Hopkins wrote:
> I've just joined this list to get answers to a few questions. I would have
> searched the archive before posting, but there seems to be no way of
> searching except via quarterly summaries. I searched the last four quarters
> without success.
> 
> I'm a SAS user, but a few years ago I tried the mixed model in R, with the
> help of an R user (Alice Sweeting). At that time, the lme package did not
> provide standard errors for the variances, nor did it allow negative
> variance. Have these limitations been addressed?  As I recall, Alice found
> some code that provided standard errors, but it gave values different from
> those of the mixed model in SAS (Proc Mixed). I therefore opted to stay with
> SAS, because allowing for negative variance for random effects other than
> residuals and estimating uncertainties in variances are both fundamental to
> mixed modeling, in my view. I also became fluent with SAS coding over the
> years and did not want to make the effort with R coding. Interestingly, SAS
> introduced a free cloud version called SAS Studio, evidently modeled on R
> Studio, to try to win back customers!
> 
> Will
> 
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