[R-sig-ME] help interpreting variance estimate in nlmer
robert denham
rjadenham at gmail.com
Tue Oct 16 02:19:48 CEST 2012
Hi,
just a quick question to try and help me understand the
interpretation of the variance part of a non-linear mixed effects
model.
If I use the orange tree example like:
nm1 <- nlmer(circumference ~ SSlogis(age,Asym, xmid, scal) ~ Asym|Tree,
Orange, start=c(Asym=192,xmid=770, scal=120),
corr=FALSE)
I get:
> summary(nm1)
Nonlinear mixed model fit by the Laplace approximation
Formula: circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym | Tree
Data: Orange
AIC BIC logLik deviance
1901 1908 -945.3 1891
Random effects:
Groups Name Variance Std.Dev.
Tree Asym 53986.025 232.349
Residual 52.868 7.271
Number of obs: 35, groups: Tree, 5
Fixed effects:
Estimate Std. Error t value
Asym 192.04 104.09 1.845
xmid 727.89 31.97 22.771
scal 347.97 24.42 14.252
So a single random effect for the asymptote. The values of these for
each tree are:
> ranef(nm1)
$Tree
Asym
3 -37.925218
1 -30.143951
5 -5.299054
2 32.352094
4 41.016208
But the estimated variance/sd of this seems very large (sd=232.349).
Should we be able to interpret the estimates as the random effect term
for the asymptote is distributed N(0,232.349) and that the estimated
random effects should look like they come from this distribution?
Basically, I don't really understand the relationship between the
distribution of ranef(nm1)$Tree and the estimated std.dev of the tree
(232.349). I would love it if someone could help explain it.
Thanks
Robert
More information about the R-sig-mixed-models
mailing list