effectplots

Effect plots are graphical representations used to illustrate the estimated influence of one or more predictor variables on a response variable within a statistical model. They summarize how the predicted outcome changes as a variable of interest varies, while other variables are held constant or averaged over. They are used in regression analysis, analysis of variance, and machine learning to aid interpretation of model results.

Types commonly encountered include main effects plots, which show the effect of a single variable, typically with estimated means or predicted values at representative values of other variables; interaction plots, which display how the effect of one variable changes across levels of another; and partial dependence plots, which show the average predicted response as a function of one or more features, marginalizing over the others.

Interpretation focuses on slope, curvature, and the crossing of lines. A steeper slope indicates a stronger effect; nonlinearity or non-parallel lines suggest interactions or non-additive effects. Partial dependence plots reveal nonlinear relationships but assume some degree of independence between features, which may not hold in finite samples. Construction often involves fitting a model and generating predicted values over a grid of values for the variables of interest. In practice, effect plots support model diagnostics, communication of results, and exploratory data analysis, while being sensitive to scale, centering, and the range of the variables.

Software commonly provides functions to create effect plots, including packages for regression, generalized additive models, and various machine learning frameworks.