I am analyzing a dataset on the effects of six pesticides on population growth rate of a predatory mite. The response variable is the population growth rate of the mite (ranges from negative to positive) and the exploratory variable is a categorical variable (treatment). The experiment was blocked in time (3 blocks / replicates per block) and it is unbalanced - at least 1 replicate per block. I am analyzing the data in nlme using model<-lme(growth.rate~treatment,random=~1|block). When I ran intervals(model), the confidence intervals of the variance of the random factor range from 0 to inf. Any comments as to why I get unrealistic confidence intervals for the random factor?
In another study, I am investigating the interactions between pesticides in a two-way design: (pesticideA x no pesticide A) crossed with (pesticideB x no pesticide B). The blocking is as above, and the data are unbalanced again. The model is defined as model<-lme(growth.rate~pestA*pestB,random=~1|block). When I run intervals (model), I usually get the following error message: "Error in intervals.lme(model) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate variance-covariance". I have read on the mailing list that the error message appears when the model is not well-specified, but I do not see an alternative way of specifying the model. Any ideas as to why I get wide confidence intervals or the error message? Any recommendations on other possibilities for analyzing the data are greatly appreciated. Thank you very much, Menelaos Menelaos Stavrinides Lecturer, Dept. of Agricultural Sciences Cyprus University of Technology P.O. Box 50329, 3603 Limassol, Cyprus Tel.: + 357 25002186 Fax: + 357 25002767 Email: m.stavrini...@cut.ac.cy <html> <body> <img src="http://www.cut.ac.cy/images/environmentalSign.gif"/> </body> </html> [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
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