I am having an issue with lmer that I wonder if someone could explain.
I am trying to fit a mixed effects model to a set of longitudinal data
over a set of individual subjects:
(fm1 <- lmer(x ~ time + (time|ID),aa))
I quite often find that the correlation between the random effects is 1.0:
Linear mixed model fit by REML
Formula: x ~ time + (time | ID)
Data: aa
AIC BIC logLik deviance REMLdev
28574 28611 -14281 28561 28562
Random effects:
Groups Name Variance Std.Dev. Corr
ID (Intercept) 77.035 8.7770
time 10.817 3.2889 1.000
Residual 112.151 10.5901
Number of obs: 3539, groups: ID, 1000
Fixed effects:
Estimate Std. Error t value
(Intercept) 98.7601 0.3894 253.64
time 1.3671 0.2001 6.83
Correlation of Fixed Effects:
(Intr)
time -0.045
All other parameters seem to converge as I increase the size of the
data set, or have a reasonable distribution over several bootstrap
samples. This suggests to me there is a singularity or something in
solving for the random effects correlation. Does anyone have any
insight?
Thanks,
Kurt Smith
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