Hi,

I understand that dichotimization of the predicted probabilities after

logistic regression is philosophically questionable, throwing out

information, etc.

But I want to do it anyway. I'd like to include as a measure of fit %

of observations correctly classified because it's measured in units

that non-statisticians can understand more easily than area under the

ROC curve, Dxy, etc.

Am I right that there is an optimal Y>=q probability cutoff, at which

the True Positive Rate is high and the False Positive Rate is low?

Visually, it would be the elbow in the ROC curve, right?

My reasoning is that even if you had a near-perfect model, you could

set a stupidly low (high) cutoff and have a higher false positive

(negative) rate than would be optimal.

I know the standard default or starting point is Y>=.5, but if my

above reasoning is correct, there ought to be an optimal cutoff for a

given model. Is there an easy way to determine that cutoff in R

without writing my own script to iterate through possible breakpoints

and calculating classification accuracy at each one?

Thanks in advance.

-Dan