Hi everyone,
I'm trying to fit a longitudinal model with natural cubic splines with 2
inner knots,
where I want to assume a diagonal covariance matrix for the random effects
(i.e.
uncorrelated random effects). Let's say, I'm using the well-known data
"sleepstudy"
from the "lme4" package.
First, I run the model trough "nlme" package:
model.nlme <- lme(Reaction ~ ns(Days, df = 3),
random = list(Subject = pdDiag(form = ~ ns(Days, df =
3))),
data = sleepstudy)
An the output indicates that there is no correlation between random
effects:
Random effects:
Formula: ~ns(Days, df = 3) | Subject
Structure: Diagonal
(Intercept) ns(Days, df = 3)1 ns(Days, df = 3)2 ns(Days, df = 3)3
Residual
StdDev: 25.78 57.12 63.62 46.61
20.97
However, I do not know how to run the same model under lme4 package. I
tried:
model.lme4 <- lmer(Reaction ~ ns(Days, df = 3) + (ns(Days, df = 3) ||
Subject),
data = sleepstudy)
But, as shown by the output, I only have independence regarding the random
intercept effect (which, by default, is not included in the B-spline
basis):
Random effects:
Groups Name Variance Std.Dev. Corr
Subject (Intercept) 605.9 24.62
Subject.1 ns(Days, df = 3)1 3210.5 56.67
ns(Days, df = 3)2 4183.9 64.68 0.57
ns(Days, df = 3)3 2296.3 47.93 0.44 0.72
Any guidance on this issue would be much appreciated.