Generally it is best to send questions like this to the
R-SIG-Mixed-Models@R-project.org mailing list (see instructions at
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models).  Many of
those who read that list can reply and often much faster than I am
able to.

I am taking the liberty of cc:ing the list on this reply.


On Sun, Aug 25, 2013 at 10:07 AM, Steven Tzvi Pirutinsky <
stevenpirutinsky@gmail.com> wrote:

> Hi Professor Bates,
>
> If I can impose, can you provide any material related to the following
> situation:
>
> 1) Large sample (~1,110),  9 waves (~70 months), modeled using nlme
>
> 2) M1: Linear time as a fixed and random effect fits well
>
> 3) M2: Adding quadratic time as fixed and random improves fit (LL and BIC)
>
> 4) However, approximate variance/covariance matrix for M2 is non-positive
> definite and correlation between linear and quadratic factors r ~ .94
>
> 5) I suspect that linear growth is more appropriate and that the quadratic
> effect is due to a ceiling effect as scores rise over time (scale mean at
> intercept is 3.1, max is 5).
>
> Any leads in terms of literature or even terminology to describe this in a
> paper?
>
> Thanks!
> Steve
>
>
> --
> Steven Tzvi Pirutinsky, M.Phil.
> Ph.D. candidate, Clinical Psychology
> Department of Counseling and Clinical Psychology
> Teachers College, Columbia University
> Box 303, 525 West 120th St., New York, NY 10027
> E: SP2813@columbia.edu
> http://scholar.google.com/citations?user=yO6-dsEAAAAJ
>
> "If it was so, it might be; and if it were so, it would be; but as it
> isn't, it ain't. That's logic." - Tweedledee, Through the Looking-Glass,
> Lewis Carroll
>

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