Hi Ben

Thanks for the reply, and for confirming that R has no NOBOUND equivalent.

By 'handling the negative variance in a way that including Time as a random
factor in the usual way would not', I simply meant that including Time as a
random factor but *not* using the NOBOUND option would result in an implied
*positive* correlation (not what we want) between the two Type
measurements from the same Group and Time (unless further correlation
structure was specified) - sorry if my phrasing was confusing.

As to what the advantages are of doing it the 'SAS way', I think my
colleague suggested it as I was unable to obtain convergence in my R model
if I also included Time as a fixed factor (although as we have no real
reason to believe that either of the measurements should vary systematically
with timepoint, am I right in thinking that including Time only in the
correlation statement shouldn't be a major problem?).  Beyond this, I agree
it does sound a bit kludge-like, but I wondered if I was missing something
;-)

Thanks again for your help

Regards,
Carrie

*> 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"?*

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