High correlation among random effects for longitudinal model
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 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
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