effects (version 4.1-3)

effects-package: Effect Displays for Linear, Generalized Linear, and Other Models


Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors.



Package: effects
Version: 4.1-3
Date: 2019-09-04
Depends: R (>= 3.5.0), carData
Suggests: pbkrtest (>= 0.4-4), nlme, MASS, poLCA, heplots, splines, ordinal, car, knitr, betareg, alr4
Imports: lme4, nnet, lattice, grid, colorspace, graphics, grDevices, stats, survey, utils, estimability
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
URL: https://www.r-project.org, http://socserv.socsci.mcmaster.ca/jfox/

This package creates effect displays for various kinds of models, as partly explained in the references. Typical usage is plot(allEffects(model)) or plot(predictorEffects(model)), where model is an appropriate fitted-model object. Additional arguments to allEffects, predictorEffects and plot can be used to customize the resulting displays. The function effect can be employed to produce an effect display for a particular term in the model, or to which terms in the model are marginal. The function predictorEffect can be used to construct an effect display for a particularly predictor. The function Effect may similarly be used to produce an effect display for any combination of predictors. In any of the cases, use plot to graph the resulting effect object. For linear and generalized linear models it is also possible to plot partial residuals to obtain (multidimensional) component+residual plots. See ?effect, ?Effect, ?predictorEffect, and ?plot.eff for details.


Fox, J. and S. Weisberg (2019) An R Companion to Applied Regression, Third Edition Sage Publications.

Fox, J. (1987) Effect displays for generalized linear models. Sociological Methodology 17, 347--361.

Fox, J. (2003) Effect displays in R for generalised linear models. Journal of Statistical Software 8:15, 1--27, <http://www.jstatsoft.org/v08/i15/>.

Fox, J. and R. Andersen (2006) Effect displays for multinomial and proportional-odds logit models. Sociological Methodology 36, 225--255.

Fox, J. and J. Hong (2009). Effect displays in R for multinomial and proportional-odds logit models: Extensions to the effects package. Journal of Statistical Software 32:1, 1--24, <http://www.jstatsoft.org/v32/i01/>.

Fox, J. and S. Weisberg (2018). Visualizing Fit and Lack of Fit in Complex Regression Models: Effect Plots with Partial Residuals. Journal of Statistical Software 87:9, 1--27, <https://www.jstatsoft.org/v087/i09>.