I have been searching for the answer online without success.I have a full factorial design with temperature (Cold and Warm), predator (Yes/No), and aphid species (A, B and C) as fixed effects (12 treatments in total). I did 3 replicates of each treatment at 5 different dates (a total of 15 replicates per treatment). This was a lab experiment performed with only two growth chambers (corresponding each to one temperature) which were inverted at each date (for example: Date 1, chamber 1: Cold, chamber 2: Warm; Date 2, chamber 1: Warm, chamber 2: Cold;….). The response variable is aphid abundance.
Date is a random effect but I am not sure how should I deal with the temperature/chamber component and also the fact that predator and species are nested in the temperature regime:
m1.nlme = lme(aphid ~ temperature*predator*species, method="REML", random = ~ 1|date, data = My.Data) m2.nlme = lme(aphid ~ temperature*predator*species, method="REML", random = ~ 1|date/temperature, data = My.Data) m3.nlme = lme(aphid ~ temperature*predator*species, method="REML", random = ~ 1|date/temperature/predator/species, data = My.Data) I am a bit confuse with all the things I red online and do not know which one of these models is the best for these data?
Best Answer
So, you want to model one effect, date (see comments below). And three predictors. Based on what I know now I would say this is the way to go:
m.nlme = lmer(aphid ~ temperature*predator*species + 1|date, data = My.Data) This model assumes no nested structure. What part of the design makes you want to use a nested structure?
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