Søren Højsgaard [EMAIL PROTECTED] writes:
Dear all,
In the SASmixed package there is an example of an analysis of a split-plot
experiment. The model is
fm1Semi - lme( resistance ~ ET * position, data = Semiconductor, random = ~ 1 |
Grp)
where Grp in the Semiconductor dataset is
I have been asked how to handle the following situation in R:
Given an unbalanced design of 3 crossed random effects, such as
subject, rater and item, how to estimate the variance components?
I know how to do it using lme, but this seems to be limited to
the nested case; or to use aov with error
Wilhelm B. Kloke [EMAIL PROTECTED] writes:
I have been asked how to handle the following situation in R:
Given an unbalanced design of 3 crossed random effects, such as
subject, rater and item, how to estimate the variance components?
I know how to do it using lme, but this seems to be
Hi,
here is a clarified version of my problem.
I have a total of 4*74 observations on 74 different
mums in 5 different populations of mums, subject to 6
treatments (2 moisture levels*3 substrate types).
I want to know if mum interacts with moisture level or
substrate type.
Population, moisture
Have you studied Pinhiero and Bates (2000) Mixed Effects Models in S
and S-Plus (Springer)?
Also, have you tried simplifying your lme call until you get
something that works, then start adding back terms in various
configurations until it breaks?
Have you tried to compute how many
Hi,
thanks for the advice. I have looked at the Pinheiro
and Bates book and I've tried simplifying my model.
I've narrowed the problem down to having mum nested
within pop. If I run the analysis on each population
separately, the interaction between mo and su with mum
works fine.
If I could
How many mum's and pop's do you have, and how many observations do
you have of each mum-pop combination? If you want mum nested within
pop, do I infer correctly that each mum has mated with only one pop, but
that each pop may have offspring by multiple mums? The table of mum-pop
Hi,
I have a data set on germination and plant growth with
the following variables:
dataset=fm
mass (response)
sub (fixed effect)
moist (fixed effect)
pop (fixed effect)
mum (random effect nested within population)
iheight (covariate)
plot (random effect- whole plot factor for split-plot
Hi,
if I have posted this twice, please ignore this. I'm
not sure if I sent it to the correct e-mail address
the first time.
I have a data set on germination and plant growth with
the following variables:
dataset=fm
mass (response)
sub (fixed effect)
moist (fixed effect)
pop (fixed effect)
mum
