Possible warning: basic question ahead.
Let's say that I model whether I wear red shoes depending on the weather. Red shoes, which is my dependent variable, is a dichotomous variable as I either wear them or don't. Weather is a variable with five 'levels' and I'm trying to find the probability that I will wear read shoes.
Let's say I model out this relationship with a logistic regression model in R:
mod = glm(shoes ~ weather, data=mydat, family=binomial(link="logit"))
Now, what I am interested in is finding what are the probabilities for wearing red shoes for each of the five 'levels' in weather. So perhaps I might have a 20% probability of wearing red shoes when cold, 30% when mild, 40% when warm, and so forth.
I'm wondering if modeling is a requirement for finding this information? If so, how does one go from somewhat meaningful regression coefficients to meaningful probabilities in R?