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)? > > 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] > > > > > > http://mobile.yahoo.com.au - Yahoo! Mobile > > - Check & compose your email via SMS on your > Telstra or Vodafone mobile. > > > > ______________________________________________ > > [EMAIL PROTECTED] mailing list > > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > http://mobile.yahoo.com.au - Yahoo! Mobile - Check & compose your email via SMS on your Telstra or Vodafone mobile. ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help