I have a traditional logit model with a dichotomous dependent variable and several independent variables. One of the IVs is categorical and in my R code, I treat it as a factor.
In the past, I've presented marginal effects by creating a simulated dataset in which I hold all variables at their mean or median and then increment the variable of interest over its range. I then use the estimated model to get the predicted probability of a given outcome over the range of the variable of interest. This is then easily plotted to create a nice graphical interpretation of the estimated effect of changes in the variable of interest.
But in the fixed effect case, where one variable in the model is a factor, how does one generate an analogous graph?
Do I need a separate estimated effect graph for each value of the factor variable or is there someway to estimate an average effect of the changes in the variable of interest over all the categories in factor variable while all other variables are held at their mean?