I have a dataset "Xs" and also some model coefficients. I would like to score the dataset using the coefficients. The dataset has 7 items and there are 8 coefficients counting the intercept. What is the best way to perform this task.

So, I would like a vector from something like this:

log_odds = Bo + B1(X1) + B2(X2),...,+ B7(X7).

After I have that vector I am going to calculate predicted probabilities from it, then calculate Sensitivities, 1 - Specificities, and then the AUC and plot curve using the Y vector I have.

There's probably a more elegant way, but since you only have 7 predictors, it's not too bad to write it out manually (also helps confirm explicitly it's doing what you want)

I am sure I will clean this up at some point, but the following is my current code to plot an ROC curve based on a binomial model's coefficients. I am planning in the future to compare curves for multiple modeling approaches in the same graph, but I don't currently have time to play around with it. I will update this thread if I remember and make any progress.