My experimental design is roughly this: I have observations of 5 categories of behavior in a specific situation, with two individuals interacting at a time. In each dyad, I only collect data from one individual. Each time that situation is observed, I categorize the presence/absence of the 5 behaviors. The behaviors are not mutually exclusive. I also register: 1) if the individuals are facing each other or not (as this might influence the focal behavior), 2) what is the reaction of the non-focal individual, and 3) the intensity of the interaction at that point. I have 19 individuals and 248 instances of the situation I'm studying. What we want to know is if these 5 behaviors of the focal are influencing the non-focal or not, and what are the variables that have a stronger influence on this.
So I organized my variables like this:
- n: 248 instances of the situation
- predictor variables: facing each other (yes or no), non-focal reaction (a or b), interaction intensity (1 out of 8 categories)
- outcome variables: 5 behaviors
- random variable: individuals
Can I use a GLMM with this type of data/experimental design?
I have a lot of missing values, since observations where not always done in good visibility conditions. I can either organize the outcome variables as present/absent (which would be binomial data, but would mean that I have 5 outcome variables) or I can combine them into a single categorical outcome variable, but would lose information about missing values, because missing values would either count as absences (and I think this is not correct) or I would have to dismiss a lot of my data. So is it possible to apply GLMM with any of these situations? If I go for the binomial data, I was under the impression that I would have to run models for each category of behavior separately?
If I can use GLMM with the behaviors in categories, which would be the function and family to use?
If the binomial data is more correct, I know the functions to use, but how do I connect the 5 categories of behavior for interpretation purposes?