ENH: graphic diagnostic binary endog - performance of predicted probabilities

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python
Domain
analytics

Research direction

No files or tests are named. Start by reviewing issue #4262, the cited calibration paper, and the existing binary-response prediction functionality; the count-data notebook may provide related context. Done requires an agreed diagnostic scope and implementation plan for comparing predicted probabilities with observed proportions or frequencies.

Written by the indexing model from the issue text.

Description

comp-discrete comp-genmod comp-graphics topic-diagnostic type-enh

Looks interesting (partial skimming)

perfomance of Logit or similar in terms of predicted probabilities and how well they match observered proportions, proportions/frequencies by exog pattern or by some kind of grouping

I think this is mostly a special case of #4262 for binary response models,
for count data I have the plots for average prediction in a notebook

Van Calster, Ben, Daan Nieboer, Yvonne Vergouwe, Bavo De Cock, Michael J. Pencina, and Ewout W. Steyerberg. 2016. “A Calibration Hierarchy for Risk Models Was Defined: From Utopia to Empirical Data.” Journal of Clinical Epidemiology 74 (June): 167–76. https://doi.org/10.1016/j.jclinepi.2015.12.005.

(There likely is a larger literature, I just ran into this by chance, related articles to something else)

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