What question did this study set out to answer?

To address the difficulty of interpreting raw coefficients in complex statistical models by computing and visualizing tidy marginal effects.

June 29, 2018The Journal of Open Source SoftwareOpen Access

ggeffects: Tidy Data Frames of Marginal Effects from Regression Models

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Authors

DLDaniel LüdeckeUniversität Hamburg

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Implication

Methodological software report demonstrates marginal effects computation for complex regression models, highlighting improved interpretability and intuitive visualization of statistical associations.

Key Points

To address the difficulty of interpreting raw coefficients in complex statistical models by computing and visualizing tidy marginal effects.
Evaluated interpretability challenges associated with interaction terms, logistic regression, and nonlinear transformations such as polynomials and splines.
Developed the ggeffects package to generate consistent, tidy data frames containing calculated marginal effects for regression analyses.
Marginal effects facilitate direct, intuitive interpretation of predictor-outcome relationships where standard regression estimates are non-intuitive.
Tabulating marginal effects into tidy data structures enables streamlined, clear graphical visualization across varied model architectures.

Cite This Study

Daniel Lüdecke (2018) studied this question.

synapsesocial.com/papers/69d7331fc74376700bf3087e https://doi.org/10.21105/joss.00772

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June 29, 2018The Journal of Open Source SoftwareOpen Access

ggeffects: Tidy Data Frames of Marginal Effects from Regression Models

View Full Paper

Ask AI

Bookmark

Authors

DLDaniel LüdeckeUniversität Hamburg

Discussion

Member takes

Implication

Methodological software report demonstrates marginal effects computation for complex regression models, highlighting improved interpretability and intuitive visualization of statistical associations.

Key Points

To address the difficulty of interpreting raw coefficients in complex statistical models by computing and visualizing tidy marginal effects.
Evaluated interpretability challenges associated with interaction terms, logistic regression, and nonlinear transformations such as polynomials and splines.
Developed the ggeffects package to generate consistent, tidy data frames containing calculated marginal effects for regression analyses.
Marginal effects facilitate direct, intuitive interpretation of predictor-outcome relationships where standard regression estimates are non-intuitive.
Tabulating marginal effects into tidy data structures enables streamlined, clear graphical visualization across varied model architectures.

Cite This Study

Daniel Lüdecke (2018) studied this question.

synapsesocial.com/papers/69d7331fc74376700bf3087e https://doi.org/10.21105/joss.00772

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