interflex-package: Multiplicative Interaction Models Diagnostics and...
In interflex: Multiplicative Interaction Models Diagnostics and Visualization
Description
Details
Author(s)
References
See Also
Description
Producing Flexible Marginal Effect Estimates with Multiplicative Interaction Models
Details
This package performs diagnostics and visualizations of multiplicative interaction models. Besides conventional linear interaction models, it provides two additional estimation strategies–linear regression based on pre-specified bins and locally linear regressions based on Gaussian kernels–to flexibly estimate the conditional marginal effect of a treatment variable on an outcome variable across different values of a moderating variable. These approaches relax the linear interaction effect assumption and safeguard against excessive extrapolation.
Author(s)
Jens Hainmueller; Jonathan Mummolo; Yiqing Xu (Maintainer); Ziyi Liu
References
Jens Hainmueller; Jonathan Mummolo; Yiqing Xu. 2019. "How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice." Political Analysis, Vol. 27, Iss. 2, April 2019, pp. 163–192. Available at: https://www.cambridge.org/core/journals/political-analysis/article/how-much-should-we-trust-estimates-from-multiplicative-interaction-models-simple-tools-to-improve-empirical-practice/D8CAACB473F9B1EE256F43B38E458706.
See Also
interflex, plot.interflex, and predict.interflex
interflex documentation built on May 18, 2021, 5:06 p.m.
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Description Details Author(s) References See Also
Description
Producing Flexible Marginal Effect Estimates with Multiplicative Interaction Models
Details
This package performs diagnostics and visualizations of multiplicative interaction models. Besides conventional linear interaction models, it provides two additional estimation strategies–linear regression based on pre-specified bins and locally linear regressions based on Gaussian kernels–to flexibly estimate the conditional marginal effect of a treatment variable on an outcome variable across different values of a moderating variable. These approaches relax the linear interaction effect assumption and safeguard against excessive extrapolation.
Author(s)
Jens Hainmueller; Jonathan Mummolo; Yiqing Xu (Maintainer); Ziyi Liu
References
Jens Hainmueller; Jonathan Mummolo; Yiqing Xu. 2019. "How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice." Political Analysis, Vol. 27, Iss. 2, April 2019, pp. 163–192. Available at: https://www.cambridge.org/core/journals/political-analysis/article/how-much-should-we-trust-estimates-from-multiplicative-interaction-models-simple-tools-to-improve-empirical-practice/D8CAACB473F9B1EE256F43B38E458706.
See Also
interflex, plot.interflex, and predict.interflex
R Package Documentation
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