[R-sig-ME] High correlation among random effects for longitudinal model

[Joshua Rosenberg]

Thank you Thierry and Ben,
Josh

On Tue, Apr 3, 2018, 4:10 AM Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:

> Dear Joshua,
>
> I wrote a blog post on a similar issue a few months ago. You can read
> it here:
> https://www.muscardinus.be/2018/02/highly-correlated-random-effects/
>
> In case you have one observation per time point per individual, then
> the random effects structure and correlation structure is probably too
> complex for the data.
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
> AND FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx at inbo.be
> Havenlaan 88
> <https://maps.google.com/?q=Havenlaan+88&entry=gmail&source=g> bus 73,
> 1000 Brussel
> www.inbo.be
>
>
> ///////////////////////////////////////////////////////////////////////////////////////////
> To call in the statistician after the experiment is done may be no
> more than asking him to perform a post-mortem examination: he may be
> able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does
> not ensure that a reasonable answer can be extracted from a given body
> of data. ~ John Tukey
>
> ///////////////////////////////////////////////////////////////////////////////////////////
>
>
>
>
> 2018-04-01 14:55 GMT+02:00 Joshua Rosenberg <jrosen at msu.edu>:
> > Hi R-sig-mixed-models, I am using the nlme package (and lme() function)
> to
> > estimate a longitudinal model for ~ 270 individuals over five time
> points.
> > Descriptively, the data seems to take a quadratic form, so I fit a model
> > like the following:
> >
> > lme(outcome ~ time + I(time^2),
> >     random = ~ time + I(time^2),
> >     correlation = corAR1(form = ~ time | individual_ID),
> >     data = d_grouped)
> >
> > I have a question / concerns about the random effects, as they are highly
> > correlated (intercept and linear term = -.95; intercept and quadratic
> term
> > = .96; linear term and quadratic term = -.995):
> >
> > Random effects:
> >  Formula: ~time + I(time^2) | individual_ID
> >  Structure: General positive-definite, Log-Cholesky parametrization
> >             StdDev    Corr
> > (Intercept) 34.836512 (Intr) time
> > time        39.803783 -0.959
> > I(time^2)    8.342256  0.969 -0.995
> > Residual    28.920368
> >
> > Is this a concern in terms of interpreting the model? Is this a concern
> > technically in terms of how the model is specified?
> >
> > Thank you for pointing me in the right direction. Happy to answer any
> > follow-up questions or to share additional details and information.
> >
> >
> > Josh
> >
> > --
> > Joshua Rosenberg, Ph.D. Candidate
> > Educational Psychology & Educational Technology
> > Michigan State University
> > http://jmichaelrosenberg.com
> >
> >         [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
-- 
Joshua Rosenberg, Ph.D. Candidate
Educational Psychology ​&​ Educational Technology
Michigan State University
http://jmichaelrosenberg.com

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