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I am trying to predict the daily amount of waste per person produced in the fishery sector. We surveyed fishing boats at the end of their fishing trip and the variables I have are duration of trip (days), number of fishers, waste category and waste weight (g), boat ID.

For each fishing trip I calculated grams of waste per person per day, i.e. daily waste per capita.

To predict daily waste per capita, I am using a Gaussian mixed effect model with log(waste per capita) as response variable (I transformed it cause it was not normally distributed – and I'm not sure it's correct to do so). Explanatory variable is waste category and boat ID is a random effect.

I use the predict function to estimate daily waste per capita for each category and then back transformed it with exp(...).

My question is: is it correct to transform daily weight per capita to fit a Gaussian model?

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  • $\begingroup$ Is there a reason not to use something like a Gamma distribution to model the response? Is there a relationship between the response and some of the explanatory variables that we try to linearise? Notice that when back-transforming the additive errors become multiplicative. $\endgroup$ – usεr11852 May 10 '20 at 0:43
  • $\begingroup$ @usεr11852 I was not famiiar with the gamma distribution and after a quick read I understand that it can be used for continuous non negative positively skewed data. In that case, a gamma distribution seems appropriate, but are there any other assumptions I am missing? There is no linear relationship between response and explanatory variables. $\endgroup$ – Alessandra Bielli May 11 '20 at 1:30

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