# Logistic regression risk prediction model - poor calibration but good discrimination

I am trying to create risk prediction model in R. I am new to logistic regression risk prediction analysis. I obtained reliability curve using Cal_curve <- calibrate(Multi_model_1, method='boot', B=1000) and the results are (n=2813 Mean absolute error=0.011 Mean squared error=0.00063 0.9 Quantile of absolute error=0.022). But the reliability curve looks poorly calibrated as in the attached image.

The model internal validation looks better.

validate(Multi_model_1, B=1000)

Sample (approximately 80 events and 2730 non-events for training datasets, 6 independent variables) I used bootstrapped internal validation with all the available sample. Is it right to split the data for testing set?

Is my model good to use or some other techniques can be applied to get better reliability curve for this logistic model?

Confused how to proceed further.

• What is the “actual probability”?
– Dave
Aug 11 '20 at 1:26
• @Dave OP is using Frank Harrell's rms package. You can likely learn more from examining the documentation than OP could tell you. No offense OP Aug 11 '20 at 2:05
• @Dave. These are my probabilities Age_bin=2 0.53 Age_bin=3 0.65 Age_bin=4 0.62 A 0.65 B 0.52 C 1.00 D=1 0.67 Aug 11 '20 at 2:19