I would like to see which factors can better explain the success of a particular event to happen. I'm interested in 4 factors, being
observer. All 4 factors have only two possible outcomes (
observer, which has up to four.
I've organized my data the following way:
head(data) success before during season observer 1: no wet dry Winter 1 2: yes wet dry Winter 2 3: yes wet dry Fall 1 4: yes wet dry Fall 4 5: no wet dry Winter 3 6: no wet dry Fall 1
My idea is to look at how this factors (predictor variables) best explain the response variable (
no) to happen. I would like to test this with and without interactions between predictor variables. All models will be constructed and the one yielding the lowest AICc will be the one best explaining a "
success:yes" (e.g. success~before * after * season * observer).
Would that be the right approach given my data?
I'm unsure about what type of distribution to use, although I would assume binomial because only two possible outcomes are possible (
Any other suggestions are welcomed. Also, is it necessary to include a random effect?