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>.