
Dear list,
I recently submitted a paper in which I analyzed plant growth response to several environmental factors in several sites. I wanted to account for the variation attributable to the different sites, so I made "site" a random effect in a simple LME regression model (e.g. m<-lme(plantgrowth~nitrogen,random=~1|site,method="ML"); library nlme). I had 10 observations in each of 11 sites. Seemed fairly straightforward to me.
When reviews came back, one reviewer and the editor requested that I compute a "lack of fit" term for my regressions without which they couldn't be confident that linear models were most appropriate for my data. I have not done this before, and I have not seen anything about this for LME models when I have searched. I have only seen it for regular regression tests, but I don't think I can apply what I would find in regular regression tests to this LME model because I would lose the random effect. I know about model checking via diagnostic plots, etc, but I don't think this is what they want. They seem to want me to generate an F statistic and a p value for this "lack of fit" term. 
My questions are: 
1.) is this even possible/practical within a LME model? 
2.) if so, what steps should I take to complete this task?3.) if not, can you suggest other diagnostics that might satisfy what they're looking for?
I have a feeling this is a fairly elementary question/procedure, so I hope I am not wasting anyone's time. Please help if you can. I am happy to provide any more information that may be needed.
Please reply directly to lquinn@hotmail.com. Thank you in advance!
Lauren Quinn



   		 	   		   		 	   		  
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