Hi Everyone,

I am trying to analyze a split plot experiment in the field that was
arranged like this:
I am trying to measure the fitness consequences of seed size.

Factors (X):
*Seed size*: a continuous variable, normally distributed.
*Water*: Categorical Levels- wet and dry.
*Density*: Categorical Levels- high, medium and solo
*Plot*: Counts from 1 to 20
The *response variable *(Y) was the number of seeds produced at the end of
the season.

The experiment started 15 days after plants germinated in the field.
20 plots were chosen where there was high enough density so I could
manipulate it. In an area where artificial irrigation was possible for the
wet treatment, dry treatment was natural precip.
Water was blocked so 10 plots were wet and the other 10 were dry. Randomly
assigned.
Within those 20 plots 6 focal plants were chosen and randomly assigned the
three densities. (split plot design)
I did not control for seed size since it is continuous and normally
distributed, hoping that with 120 plants total (6 in each 20 blocks) I could
get all kind of sizes for every treatment. It worked ok.

I have been trying to analyze this with lme (library NLME). I am not quiet
sure which are my random variables. models I have used are:

m<-lme(log(fitness)~seedsize*density,random=~1|plot,data=dataset)
m<-lme(log(fitness)~seedsize+density+water,random=~1|plot,data=dataset)

I have also tried to include plot and water as random effects:

m<-lme(log(fitness)~seedsize+density+water,random=~1|plot/water,data=dataset)

I am actually not sure if I am using the right random variables here. Also
for some reason, it won't let me include seedsize*density*water triple
interaction
help!
thanks

-- 
Eugenio Larios
PhD Student
University of Arizona.
Ecology & Evolutionary Biology.
(520) 481-2263
elariosc@email.arizona.edu

	[[alternative HTML version deleted]]

