[R-sig-ME] How to correctly specify a mixed model
cmammides at outlook.com
Mon Feb 22 23:39:15 CET 2016
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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