Effect Displays in R for Generalised Linear Models

By John Fox. 2003

1,583 citations recorded by the source corpus.

This paper describes the implementation in R of a method for tabular or graphical display of terms in a complex generalised linear model. By complex, I mean a model that contains terms related by marginality or hierarchy, such as polynomial terms, or main effects and interactions. I call these tables or graphs effect displays. Effect displays are constructed by identifying high-order terms in a generalised linear model. Fitted values under the model are computed for each such term. The lower-order "relatives" of a high-order term (e.g., main effects marginal to an interaction) are absorbed into the term, allowing the predictors appearing in the high-order term to range over their values. The values of other predictors are fixed at typical values: for example, a covariate could be fixed at its mean or median, a factor at its proportional distribution in the data, or to equal proportions in its several levels. Variations of effect displays are also described, including representation of terms higher-order to any appearing in the model.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f22be9ea2989bbf690e5c84a8ad52e13789805a7

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Citations in the corpus

Direct and indirect interactions co‐determine species composition in nurse plant systems (2013, methodology): “0 (R Development Core Team) using the packages ‘car’ (Fox and Weisberg 2011) for type-II ANOVA, ‘effects’ (Fox 2003) for constructing effect plots, ‘lme4’ (Bates et al.”
Effect Displays in R for Multinomial and Proportional-Odds Logit Models: Extensions to the effects Package (2009, methodology): “Eﬀect displays for generalized linear models were introduced by Fox (1987), and an implementation in the eﬀects package for the statistical programming environment R (Ihaka and Gentleman 1996; R Development Core Team 2009) was described in Fox (2003).”
The R Commander: A Basic-Statistics Graphical User Interface to R (2005, methodology): “Dynamic three-dimensional scatterplots created by Graphs ! models 3D scatterplot... appear in a special RGL device window; likewise, e⁄ect displays created for statistical (Fox, 2003) via Models !”
Interactions Among Ecosystem Services Across Land Uses in a Floodplain (2014, methodology): “We performed multiple comparison tests (‘multcomp’ R package; Hothorn et al. 2008) and figures plots (‘effects’ R package; Fox 2003) to determine significant differences among the means.”
Pollination services are mediated by bee functional diversity and landscape context (2015, methodology): “Graphs were generated with the ‘effects’ package (Fox 2003) in R 2.”
Visualization of Regression Models Using visreg (2017, background): “CONTRIBUTED RESEARCH ARTICLE 57 rms (Harrell, 2015), rockchalk (Johnson, 2016), car (Fox and Weisberg, 2011), effects (Fox, 2003), and, in base R, the termplot function.”
An Introduction to Mixed Models for Experimental Psychology (2019, neutral): “15 One ﬁnal note regarding lmer and mixed is that models ﬁtted with both functions can be directly used for follow-up tests, planned contrasts, or plotting with the methods implemented in package lsmeans (Lenth, 2015) or effects (Fox, 2003).”
Foliar nutrient concentrations and resorption efficiency in plants of contrasting nutrient‐acquisition strategies along a 2‐million‐year dune chronosequence (2014, methodology): “‘effects’ package (Fox 2003) was used for further inspection of means and 95% confidence intervals (CI) to identify significant differences between chronosequence stages or nutrient-acquisition strategies.”
Are coastal habitats important nurseries? A meta‐analysis (2019, methodology): “We obtained and plotted the model-estimated partial means for each level of each covariate using the effects package (Fox, 2003).”
pdp: An R Package for Constructing Partial Dependence Plots (2017, positive): “Effect displays , available in the effects package (Fox, 2003), provide tabular and graphical displays for the terms in parametric models while holding all other predictors at some constant value—similar in spirit to plotmo ’s marginal model plots.”
Partitioning net interactions among plants along altitudinal gradients to study community responses to climate change (2014, methodology): “2012), car (Fox & Weisberg 2011) and effects (Fox 2003).”
Protected areas do not mitigate biodiversity declines: A case study on butterflies (2018, methodology): “We used the R package effects version 4.0‐0 (Fox, 2003) for visualizing the results.”