I have collected data from five consecutive fishing seasons (five factor levels). Each fishing season has five months within (five factor levels). Considering that I have a temporal correlation in my data (fishing season>> month), I thought it was a good idea to implement GLMM. I am attempting to fit a mixed effects model using R and lme4, but I am new to mixed models. I'd like to model the response as the probability to observe a fishing event in a day-by-day basis (presence/ absence) as a function of several continuous and categorical variables, with random effects for fishing season and month.
How do I have to consider the relationship between the variables fishing season and month? Crossed effects or nested effects?
From what I understood I have to consider crossed effects to model the relationship between fishing season and month because each month is repeated in fishing season... is this correct?
In each case, how do I code this???
Any suggestions?
I already found the answer here!!! Great answer indeed!! Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4?
