Hey everyone, I am wondering if someone could advice me on a repeated-measure mixed model analysis. So, I carried out an experiment of effect of contamination by antifouling paint on predator-prey interaction (snails and barnacles). I have the repeated-measures of number of barnacles consume through time in different treatments (AP and control) in each replicate (cages). See structure of my dataframe below. (Everything is balanced.)
str(consumption)
'data.frame': 96 obs. of 9 variables: $ tr : Factor w/ 2 levels "antifouling paint",..: 1 1 1 1 1 1 1 1 1 1 ... $ replicate : Factor w/ 16 levels "AP1","AP2","AP3",..: 1 2 3 4 5 6 7 8 1 2 ... $ time : int 0 0 0 0 0 0 0 0 27 27 ... $ cons : int 0 0 0 0 0 0 0 0 19 37 ... $ cons_pc : num 0 0 0 0 0 ... 1) My first doubt is about day 0. In day 0 there is no consumption, so I have a lot of zeros what is giving me trouble to meet a normal distribution. My doubt here is if I should/could (or not) to remove day 0 from analysis. I did some tests and removed day 0, and I got far better normal distribution. 2) I am running the following mixed-model, but I am not sure if it is right. As consumption is 0 in day 0, I am running a mixed-model with varying slope only. Also, what I want is set a model in each the slope varies randomly per replicate through time. That's what I am running: *m1 = lmer(cons_pc ~ tr*time + (time-1|replicate), data= consumption)* Alternatively, I could have: *m2 = lmer(cons_pc ~ tr*time + (1+time|replicate), data= consumption)*# intercept and slope varying per replicate *m3 = lmer(cons_pc ~ tr*time + (1|replicate), data= consumption)* # intercept only varying per replicate I think the first model is the right one, but I am not sure. Anyone could confirm? I ran all of them as a test and all of them work, and the first one is the best one (according to AIC score and LR-test). Thanks very much in advance. My best, Andre.
Visiting PhD student School of Ocean Sciences Bangor University Menai Bridge, Anglesey, UK [[alternative HTML version deleted]]