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?

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#### 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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