Hi,



I’m trying to fit a simple mixed effects model to data on a crop experiment.




height.lme1<-lme(crop.height~treatment, random= ~1|field, height)



treatment is a categorical variable.  field is included as a random variable
because the management history of each field might affect the outcome of the
trials.  Also the observations are unbalanced; different numbers of
measurements were recorded in each field.  treatment has a significant
effect on crop.height



The problem I’m having is this; crop.height is not homoscedastic with
respect to treatment.  Therefore, is it possible that the significant effect
of treatment is due to differences in variances rather than differences in
means?  Is there any way around this?



Thanks for your help Megan Douglas

	[[alternative HTML version deleted]]


