Hello,
I wanted to fit a linear mixed model to a data that is similar in
terms of design to the 'Machines' data in 'nlme' package except that
each worker (with triplicates) only operates one machine. I created a
subset of observations from 'Machines' data such that it looks the
same as the data I wanted to fit the model with (see code below).
I fitted a model in which 'Machine' was a fixed effect and 'Worker'
was random (intercept), which ran perfectly. Then I decided to
complicate the model a little bit by fitting 'Worker' within
'Machine', which was saying variation among workers was nested within
each machine. The model could be fitted by 'lme', but when I tried to
get
confidence intervals by 'intervals(fm2)' it gave me an error:
Error in intervals.lme(fm2) :
Cannot get confidence intervals on var-cov components: Non-positive
definite approximate variance-covariance
I am wondering if this is because it is impossible to fit a model like
'fm2' or there is some other reasons?
Thanks a lot!
Wen
#################
library(nlme)
data(Machines)
new.data = Machines[c(1:6, 25:30, 49:54), ]
fm1 = lme(score ~ Machine, random = ~1|Worker, data = new.data)
fm1
fm2 = lme(score ~ Machine, random = ~Machine-1|Worker, data = new.data)
fm2
intervals(fm2)
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