What question did this study set out to answer?

To develop and adapt graphical diagnostic methods that overcome the challenges of interpreting binary data fits in logistic regression models.

March 1, 1984Journal of the American Statistical Association

Graphical Methods for Assessing Logistic Regression Models

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Authors

JLJames M. LandwehrNew York Proton Center DPDaryl PregibonAT&T (United States)

Discussion

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Implication

Methodological study demonstrates graphical diagnostic techniques for binary logistic models, highlighting practical tools to detect model misfit and isolated anomalies.

Key Points

To develop and adapt graphical diagnostic methods that overcome the challenges of interpreting binary data fits in logistic regression models.
Adapted diagnostic displays from ordinary linear regression to accommodate the discrete structure of binary data.
Introduced three specialized graphical tools: local mean deviance plots, empirical probability plots, and smoothed partial residual plots.
Evaluated and illustrated the performance of these graphical displays using simulated and real-world datasets.
Local mean deviance plots effectively identified overall lack of model fit across data ranges.
Empirical probability plots successfully highlighted isolated departures and outliers relative to the fitted model.
Smoothed partial residual plots revealed the underlying structure and pinpointed specific causes of poor model fit.

Cite This Study

Landwehr et al. (1984) studied this question.

synapsesocial.com/papers/6a820c04933117ee2977883f https://doi.org/10.1080/01621459.1984.10477062

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Implication

Methodological study demonstrates graphical diagnostic techniques for binary logistic models, highlighting practical tools to detect model misfit and isolated anomalies.

Graphical Methods for Assessing Logistic Regression Models

Discussion

Member takes

Implication

Key Points

Cite This Study

Graphical Methods for Assessing Logistic Regression Models

Discussion

Member takes

Implication

Key Points

Cite This Study

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