I've made a binomial model using the glm function in R as:
model <- glm(formula = ym ~ year + class, family = "binomial")
Where year is an integer and class is a factor with 4 levels (class = A, B, C, D). Which gave coefficients:
Estimate Std. Error z value Pr(>|z|) (Intercept) -12.0538 33.24887 -0.753 0.4512 year 0.19302 0.01712 0.746 0.4558 classB 0.19528 0.17428 1.293 0.1961 classC 0.23886 0.15071 0.722 0.4701 classD 0.35708 0.13439 1.913 0.0557 .
Ignoring the significance of variables, I'm trying to interpret the coefficients from the model before I go on to improve it. I believe the effect of each is logit(p) = intercept + classAx + classBx + classCx + classDx, where x = 1 only for the class the individual belongs to.
First, what is the coefficient for classA (which has been removed) here? is it -(0.19528+0.23886_0.35708), the negative sum of the other coefficients of class?
Second, while I understand year can be interpreted as an increase of year by one unit corresponds to an increase in logit(p) of 0.19302, for the classes would it simply be membership to class C increases logit(p) by 0.23886?