I am trying to predict the result of an experiment (binary dependent variable) based on a number of continuous independent variables. When I do this using a largish model (9 main effects + 2 factor interaction) it seems to "work" meaning I get a plot where the predicted values are 0/1. However, if I reduce the model to say just two main effects + interaction, I get predicted values ranging from 0 - 1 (and several points in between). I have tried using probit and logit with
glm() in R.
With the smaller model I get no warnings / errors. With the larger one I get:
Warning messages: 1: glm.fit: algorithm did not converge 2: glm.fit: fitted probabilities numerically 0 or 1 occurred
What causes this behaviour?
How can I determine which of the main effects and/or interaction terms are the most important (they all have
*** in the summary)?