Biostatistics Weekly Seminar

Predictor effect plots display the response surface of a complex regression model (such as a linear or generalized linear model with interactions, predictor and response transformations, polynomial terms, or regression-spline terms) as a series of 2D graphs, focusing in turn on each predictor in the model and averaging or conditioning over the other predictors. Adding partial residuals to these graphs and smoothing the residuals can reveal lack of fit---that is, failure in the functional form of the model---and point the way toward improvement of the model. The methods described are implemented in a very general manner in the effects package for R.

This talk describes joint work with Sanford Weisberg that appears in a recent paper: J. Fox and S. Weisberg, "Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals," Journal of Statistical Software, 87(9): 1-27.

MRBIII, Room 1220
24 April 2019
1:30pm