Hi everyone

I'm trying to analyse a dataset (in nlme) where - within group -
measurements are taken at two different times.  Each measurement occasion
yields two values (one at each level of a 2-level factor, called Type) that
are negatively correlated.  I have been handling this negative correlation
by using

correlation = corCompSymm (form = ~ Type | Group / Time)

However, I've been advised by a SAS-using colleague that in SAS he would
simply include Time as a 2-level random factor (presumably nested or crossed
with Group) then use the the NOBOUND option in PROCMIXED to remove the
boundary constraints on estimates and allow estimated variance parameters to
be negative - this would handle the negative variance in a way that
including Time as a random factor in the usual way would not.

So my question is whether there is an equivalent of NOBOUND in R (in nlme or
lme4 - or in any other library that does linear mixed models)?  I have
looked at the help files but can't see anything.  Also, if so, which is
better - the NOBOUND approach or my current approach of specifying a
correlation structure?

Any help would be much appreciated!  And if this question has been asked
before I apologise, but I couldn't find anything when I searched.

Regards
Carrie

	[[alternative HTML version deleted]]


