Logistic regression with only categorical predictors So I started off with a model which included both continuous and categorical predictor variables. But now I wanted to drop the only continuous variable (distance to shore), because to my opinion it was too much correlated with other variables (habitattype). So that leaves me with only categorical predictors and there interaction terms in the model, but is that actually allowed for binomial regression?
If so, what is a good way to visualize the results? The predicted values over a predictor (categorical) is going to give strange graphs because of the limited categories.
 A: Yeah, it's perfectly acceptable for a logistic regression to contain only categorical predictors. Remember that we code categorical predictors numerically (e.g., 0 and 1, -1 and 1, etc.), so the distinction between categorical and continuous doesn't really exist for the regression.
As for how to plot the effect, I would typically use a bar plot with each bar representing the estimated probability of observing a specific outcome in that condition. These estimates can be calculated using the coefficients of the model. Remember that while a logistic regression produces estimates in logit space, with an inverse logit transformation you can turn predicted values into probabilities.
The below graph actually plots the proportions (so what was observed in the data that was subsequently analysed with a logistic regression, rather than what was estimated by the regression), but it should convey the general idea.

A: It certainly is allowed. I would suggest you choose a new approach to visualizing your data, other than the sigmoid curve.
As you said being categorical means you do not have a range of data to assess your probabilities over. Categorical variables are either there or not. One approach that comes to my mind, is to plot circles or squares whose size is proportional to your category's parameter (the $\beta$ or $\theta$). I.e in case of gender, 1 female, 0 male, then your $\beta_{female}$ might be say 0.5. Then print a circle whose radius is 0.5, and you can compare other features' importance in predicting your class. Alternatively you can use a pie chart, again to show importance. Using squares has the benefit that you can show the marginal rate of substitution too.
Assume you have the following data:
      gender_female   likes_plum   likes_peach   label

1           1              1            0          1

2           1              0            0          0

3           0              0            1          1

...

Then after your regression, you might have: $\beta_{female}=0.5,\beta_{plum}=1.4, \beta_{peach}=1$. Then if you draw squares whose sides are equal to the $\beta$s you can show how many of each $\beta$ fits in the other, effectively showing their relative importance. I.e. for our example being a peach lover has double the effect of being a female in being classified as class 1.
