Solved – Logistic regression prediction validation

I am try to evaluate a logistic regression with new data using R. I have created the logistic regression using the old data and evaluated that using chi^2 etc. i.e.

model <- glm(outcome ~ input + 0, data =, family = binomial()) 

(note I do need the intercept = 0). And then the predicted probabilities:

predicted.probabilities <- predict(model,, type = "response") 

How can I now perform the same sort of tests with these new probabilities? and what are the best tests to do here?

IMO, the following two tests are a good start:

1) A regression of the test outcomes on the predicted probabilities. If your predictions are correct on average ("calibrated"), the fit of this regression should be the 45° line.

2) Two separate histograms of the predicted probabilities: One for the test cases with y = 1, and one for the test cases with y = 0. If your model's predictions are sharp, the two histograms should differ a lot, with the former histogram focussing on high predicted probabilities and the latter focussing on low probabilities.

For more stuff along these lines, see Chapter 3 of

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