lme4 random effects for repeated measures, unbalanced data
On 16-07-19 07:28 PM, Ann Marie Raymondi wrote:
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!
Include Year as a fixed effect. You could try to include (Year|Plot), but it will overlap with the residual error (since each plot is measured once per year), so it probably won't work (without some more fussing around). You'll probably get most of the signal by including Year as a fixed effect.