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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 before, during, season and observer. All 4 factors have only two possible outcomes (before: wet and dry; during:wet and dry, season: Winter and Fall) except 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 (success: yes or 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 (yes and no).

Any other suggestions are welcomed. Also, is it necessary to include a random effect?

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  • $\begingroup$ Logistic regression should do fine. What is the interpretation of the "observer" variable? $\endgroup$ Nov 20, 2019 at 17:22

1 Answer 1

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Your can deal with your classification problem via logistic regression and compare your fitted models via their Classification Rate (or ROC Curve) on your test subset.

Regarding the response variable I would use a binomial distribution with n=1, so actually a Bernoulli distribution.

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