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So I conducted an experiment that looked at the effect that a range of temperatures had on the weight of shellfish. There are 7 temperature treatments, each temperature is 1 tank (no replicates, I know its a problem). I took samples twice a week for 1 month (each sample was destructive and approximately 20 randomly sampled organisms). I am trying to find at what sampling day did the changes between treatments become statistically different from each other. My thought is to use a repeated measures general linearized model to test day against size with pH as a fixed factor that interacts with day.

so this is what my r code looks like:

glm(weight ~ Day + Temp + Day*Temp, data = Temp1

Does this make sense for what I am trying to do?

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In a repeated measurement design you need to account for autocorrelation, so I would suggest using a mixed model.

library(lme4)
lmer(weight ~ Days * Temp + (1 + Days | fishID), data = temp1)

Days * Temp automatically expands to Days : Temp + Days + Temp.

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  • $\begingroup$ That is a good point thank you! I was initially thinking of pooling all my organismal data together for each treatment since they are not independent of one another, and I was concerned about psuedoreplication. Which means I would not have a subject effect, any thoughts on that? $\endgroup$
    – Jjohn2019
    Nov 28, 2018 at 18:05
  • $\begingroup$ Do you mean your shellfishes by "subject"? If fishID refers to your shellfishes, you can use e.g. ranef() to explore your random effects (which gives you - in this case - the individual differences in weight from the total mean weight as estimated by the model). If you have time-varying predictors, you could fit a "random-effect-within-between model " (see strengejacke.github.io/mixed-models-snippets/…), which gives you estimates on the changing within- and between-subject-effects over time. $\endgroup$
    – Daniel
    Nov 29, 2018 at 11:12
  • $\begingroup$ Yes I do thanks so much, I will explore these ideas further, this has been really helpful $\endgroup$
    – Jjohn2019
    Nov 29, 2018 at 18:27

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