So I ran a binomial glm to look at the effect of minimum temperature (continuous data) and moon phase (categorical data with 3 categories) on lion incidents. I removed the intercept to look at all 3 categories of moon by using + 0 in my glm. These were the results:
I then calculated the odd ratios and confidence intervals using the code:
exp(cbind(OR = coef(GLMoon), confint(GLMoon)))
which gave me this:
I'm not fully clear on how to interpret these as most of the odds ratios I've seen have been above 1, but from what I've read I guess it would mean that the odds for incidents during the 'new.moonthe rest' phase are 45% lower than during other phases? And that holding moon phases at a fixed value, we will see a 1.14% increase in the odds of an incident, for a one-unit decrease in temperature?
I'd prefer to calculate probabilities of attack, but I'm unsure how to do this from the coefficients or odds ratios when the intercept is removed. I'd really appreciate any help!