If you can get an answer ignoring pop, then you might be able to get an answer with pop as a separate random term without specifying mum nested within pop. Also, I'd check very carefully the specification of nesting: I've messed that up more than once, and I'm bald now, because I tore all my hair out before I figured out what I was doing wrong. (Well, there is a slight exageration there.) Have you tried a very simple toy problem (or a published example) with nesting to make sure you can get the correct answer?
hope this helps. spencer graves
Sarah Mclean wrote:
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 analyse all of the pops at once this would be preferable because I have multiple responses and pops to test so it would take a bit of time. I'm hoping there is any easier way.
Thanks Sarah
--- Spencer Graves <[EMAIL PROTECTED]> wrote: > Have you studied Pinhiero and Bates (2000) Mixed
Effects Models in S and S-Plus (Springer)?https://www.stat.math.ethz.ch/mailman/listinfo/r-help
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 coefficients
are estimated in both fixed and random terms and evaluate whether all are
estimable? For example, with 2 factors at 2 levels each, if you
don't have all 4 possible combinations, you can't estimate the
interaction -- even if you have thousands of replications of each.
Finally, you can always try to read the code. I've learned a lot about S-Plus / R by doing that -- and solved a lot
of my own problems that way.
hope this helps. spencer graves
Sarah Mclean wrote:
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 (random effect nested within population) iheight (covariate) plot (random effect- whole plot factor for
split-plot
design).
I want to see if moist or sub interacts with mum
for
any of the pops, but I am getting an error
message.
This is the formula I used: fm$pmu <- getGroups(fm, ~1|pop/mum, level=2) fm$grp = as.factor(rep(1,nrow(fm))) fm$pl <- getGroups(fm, ~1|plot) fm$mo <- getGroups(fm, ~1|moist) fm$su <- getGroups(fm, ~1|sub)
fm1 <- lme(sqrt(mass) ~ iheight + moist*sub*pop,
data=fm,
random=list(grp=pdBlocked(list(pdIdent(~pl -
1), pdIdent(~pmu - 1), pdIdent(~pmu:su - 1), pdIdent(~pmu:mo - 1))))) Error in chol((value + t(value))/2) : non-positive definite matrix in chol
I know the problem is with the random interaction terms, but I don't know how to overcome this.
Any advice would be greatly appreciated. I'm new
to R
and analysis such as this.
Thank you,
Sarah Mclean [EMAIL PROTECTED]
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