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Equivalent option to 'nobound' in SAS
2 messages · Carina Salt, Ben Bolker
On 10-10-22 09:02 AM, Carina Salt wrote:
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
I don't think this is an option in nlme or lme4.
At the risk of sounding like one of those cranky R guys, it seems as
though
the solution you're using in nlme is reasonable/appropriate/mimics
the way that you actually think about the problem ("the two measurements
within a group at a particular time are negatively correlated"), while the
SAS approach is a kluge ("if I pretend one of these variances is
negative ...").
What are the advantages of the SAS-style negative variance approach?
What do you mean by "...handle the negative variance in a way that
including
Time as a random factor in the usual way would not"?