[R-sig-ME] How to correctly specify a mixed model

christos mammides cmammides at outlook.com
Mon Feb 22 23:39:15 CET 2016

Dear all,

I have a possibly naïve question on how to correctly specify a mixed 
model. I would appreciate any help you can provide.

Let’s say I have data on plant growth from several individuals from 7 
different areas (n=96), and I want to test the effect of two climatic 
variables (temperature and rain) on growth. For each of the 7 areas I 
have one measurement for temperature and one for rain. For example, the 
first few lines of my data look like this:

Individual 	Growth 	Temperature 	Rain 	Area
1 	10 	15 	300 	A
2 	12 	15 	300 	A
3 	20 	15 	300 	A
4 	16 	25 	500 	B
5 	29 	25 	500 	B
6 	10 	25 	500 	B
… 	… 	… 	… 	…

Would the following model be appropriate (in terms of the way the random 
effect is specified)?

Model <- lmer(Growth~Temperature+Rain+(1|Area), data=Data)

It was suggested to me that since I only have one measurement for each 
climatic variable per area it’s probably better to take the average of 
the plant growth for each area and run a simple regression model such as 
this: Model <- lm(AveragedGrowth~Temp+Rain, data=AveragedData).

I am right to think that in doing that I am losing information, by 
averaging my plant growth data, and I am also reducing my sample size 
(n=7) to a point that it would be too difficult to run a regression?

Hope my question makes sense.

Thank you in advance,



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