Visualizing main effects and interactions for binary logit models
Michael N. Mitchell
UCLA Academic Technology Services
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Xiao Chen
UCLA Academic Technology Services
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Abstract. This paper considers the role of covariates when using predicted
probabilities to interpret main effects and interactions in logit models.
While predicted probabilities are very intuitive for interpreting main
effects and interactions, the pattern of results depends on the contribution
of covariates. We introduce a concept called the covariate contribution,
which reflects the aggregate contribution of all of the remaining predictors
(covariates) in the model and a family of tools to help visualize the
relationship between predictors and the predicted probabilities across a
variety of covariate contributions. We believe this strategy and the
accompanying tools can help researchers who wish to use predicted
probabilities as an interpretive framework for logit models acquire and
present a more comprehensive interpretation of their results. These
visualization tools could be extended to other models (such as binary
probit, multinomial logistic, ordinal logistic models, and other nonlinear
models).
View all articles by these authors:
Michael N. Mitchell, Xiao Chen
View all articles with these keywords:
logistic regression, predicted probabilities, main effects, interactions, covariate contribution
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