[R] Very small estimated random effect variance (lme)

[Peter Dalgaard BSA]

"Remko Duursma" <den.duurs at lycos.com> writes:

> Dear R-helpers,
> 
> i get some strange results using a linear mixed-effects model (lme), of the type:
> 
> lme1 <- lme(y ~ x, random=~x|group, ...)
> 
> For some datasets, i obtain very small standard deviations of the random effects. I compared these to standard deviations of the slope and intercept using a lmList approach. Of course, the SD from the lme is always smaller (shrinkage estimator), but in some cases (the problem cases) the SD from the lme seems way too small. E.g.: SD of intercept = 0.14, SD of slope = 0.0004, SD residual=0.11. An lmList gives a slope SD of 0.07.
> 
> I have about n=6 observations per group, and about 20-100 groups depending on the dataset.
> 
> thank you for any suggestions,

It's not a shrinkage estimator it is a "subtraction estimator",
measuring the excess variance of the empirical slopes over what would
be expected from their s.e. if all (true) slopes were identical. This
can even be negative, although the parametrizations in lme() will
enforce a zero or very small variance in that case.

(There are occasional cases where a negative variance can be
interpreted, e.g. plants competing for the same growth medium, but
you're generally in trouble if the design is unbalanced.)

-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907