High correlation among random effects for longitudinal model
On Sun, 2018-04-01 at 12:55 +0000, Joshua Rosenberg wrote:
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):
I think this is an ordinary occurrence for the intercept and time trend
to be negatively correlated. The way to avoid this is to center the
time variable at a point in the middle of the series, so, instead of
setting the values of time to {0, 1, 2, 3, 4} use {-2, -1, 0, 1, 2}.
Stuart Luppescu Chief Psychometrician (ret.) UChicago Consortium on School Research http://consortium.uchicago.edu