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.stavrinides@cut.ac.cy

 



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