Convergence Error: 0 Fixed Correlations and More

Emmanuel Curis <emmanuel.curis at ...> writes:

Speaking about this factor variables coding to have uncorrelated
random effects... Following Thierry Onkelinx's advice [thanks a lot
for this advice, by the way, I think I didn't answered yet, sorry], I
wrote down the models for a similar case, and it turned out that when
having uncorrelated random effects for all levels of a covariate, then
the Y variables were also uncorrelated, but it allowed different
variances for each level.

I thought one advantage of random effects was to introduce
correlations between observations taken for the same patient, so I
wonder if this trick really does what one expects (assuming I didn't
made mistakes, and I am not the only one to have this idea about
consequences of random effects), and that specifying special random
effects covariance matrix structures is less error-prone with nlme?

I think at least one of us is confused.  Specifying uncorrelated
random effects terms doesn't (I think) mean the predictor variables are 
uncorrelated.  It means the variation of the effects of predictor
variables across groups is uncorrelated.  

  In particular, if the model specifies that the intercept term varies
across groups, then this *does* induce correlations within groups,
because the observations within the group share a random effect
(which means there is less variation within a group than across
the overall population).  Admittedly, this is the only kind of
correlation the stable branch of lme4 allows (not AR1, or
other interesting correlation structures -- only (positive)
compound symmetry.  If nlme works for your problem, by all means
use it ... but lme4 does 'real' GLMMs (not just via PQL), and
is faster than nlme for LMMs ...


 [snipped context to make Gmane happy]