[R-sig-ME] lme4 random effects for repeated measures, unbalanced data
Ann Marie Raymondi
annmarieraymondi at u.boisestate.edu
Wed Jul 20 01:28:31 CEST 2016
Hello Listers,
I have a vegetation data set that consists of 150 plots that were sampled
1-3 times over a three year period. Plots are my unit of observation and
they are unbalanced (since plots were sampled either once, twice, or three
times). I would like to use mixed-effects models in order to account for
variation in both plots and sampling year and to keep my sample size large
(instead of conducting my analysis within individual years). Here is an
example of the grouping of my data:
Plot_ID Plot Year
2012_101 101 2012
2013_101 101 2013
2014_101 101 2014
2012_201 201 2012
2013_201 201 2013
2013_301 301 2013
My response variables are cover of vegetation functional groups and
predictors include variables related to fire and treatment history.
Additionally, I am not interested in how plots change over time per se, but
rather, in aggregating sampling from all three years to increase my sample
size and to account for the spatial/temporal correlation that arises from
doing so. It is my assumption that treating plot as a random effect
(intercept only) accounts for variation that arises from potential spatial
autocorrelation, but my main question is how to account for the repeated
measures and if I need to account for the grouping of cells within sampling
years:
Potential model:
model<-lmer(response~covariates + (1|Plot) + (1|Year).
However, I know that is not appropriate to use a random effect with only
three levels, year in this case. I'm hoping for recommendations on how to
incorporate year as a random effect. Is including (Year | Plot)
recommended? And if so, how might I interpret that effect, i.e., is it
accounting for variation introduced by different sampling year or variation
in plots over sampling year?
Thank you in advance for any help/suggestions!
Ann Marie
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