R help - Mar 2006 - An lme model that works in old R.2.1.1 but not always in R.2.2.0 - why?

Following lme model runs fine in general under R.2.1.1 but only for 9 out
of my 11 response variables under R.2.2.0.

model for one of my response variables:
lme(Yresp~F1fix,random=list(const=pdBlocked(list(~F2mix-1,~Ass:F1fix-1,~F3mix-1,~F1fix:F3mix-1,~F2mix:F3mix-1),pdClass="pdIdent")))

Yresp is my response variable, F1fix is a fixed effect factor whereas
F2mix and F3mix are random effect factors.
const is set to rep(1,dim(Ycont)[1]).

The strange thing is that if an intercept is omitted (F1fix-1) the R.2.2.0
also runs a 100 %. It's the same model, just with another
parameterization??????

Niels Sommer

On 3/22/06, Niels A. Sommer <nas at imm.dtu.dk>
wrote:
> Following lme model runs fine in general under R.2.1.1 but only for 9 out
> of my 11 response variables under R.2.2.0.
>
> model for one of my response variables:
>
lme(Yresp~F1fix,random=list(const=pdBlocked(list(~F2mix-1,~Ass:F1fix-1,~F3mix-1,~F1fix:F3mix-1,~F2mix:F3mix-1),pdClass="pdIdent")))
>
> Yresp is my response variable, F1fix is a fixed effect factor whereas
> F2mix and F3mix are random effect factors.
> const is set to rep(1,dim(Ycont)[1]).
>
> The strange thing is that if an intercept is omitted (F1fix-1) the R.2.2.0
> also runs a 100 %. It's the same model, just with another
> parameterization??????
The first thing to do in such a case is to request verbose output from
the optimizer by adding control = list(msVerbose = TRUE) to your call
to lme.  One thing that changed for lme between R-2.1.1 and R-2.2.0 is
that the default optimizer is now nlminb.  Previously it was optim.

With a model of that complexity you may well find that you are not
getting convergence either in R-2.1.1 or in R-2.2.0.  It is just that
you are learning about it in R-2.2.0