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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