Visualization of Regression Models Using visreg

By Patrick Breheny, Woodrow Burchett. The R Journal, 2017

928 citations recorded by the source corpus.

Regression models allow one to isolate the relationship between the outcome and an explanatory variable while the other variables are held constant. Here, we introduce an R package, visreg, for the convenient visualization of this relationship via short, simple function calls. In addition to estimates of this relationship, the package also provides pointwise condence bands and partial residuals to allow assessment of variability as well as outliers and other deviations from modeling assumptions. The package provides several options for visualizing models with interactions, including lattice plots, contour plots, and both static and interactive perspective plots. The implementation of the package is designed to be fully object-oriented and interface seamlessly with R’s rich collection of model classes, allowing a consistent interface for visualizing not only linear models, but generalized linear models, proportional hazards models, generalized additive models, robust regression models, and many more.

Subfields: Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/76c07451e5615643d901c9fb61b8ff93ce2360db

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. E. Aalto
2. Ryan A. Peterson
3. Miguel I. Aguirre-Urreta
4. Mikko Rönkkö
5. Henni Tenhunen

In Statistics and Probability

1. E. Aalto
2. Ryan A. Peterson
3. Miguel I. Aguirre-Urreta
4. K. Raulund-Rasmussen
5. Leena Finér
6. Mikko Rönkkö
7. Henni Tenhunen
8. L. Vesterdal

Citations in the corpus

• Finding Optimal Normalizing Transformations via bestNormalize (2021, methodology): “We utilize the visreg package (Breheny and Burchett, 2017) to perform our visualizations, using predict.”
• Is Tree Species Diversity or Species Identity the More Important Driver of Soil Carbon Stocks, C/N Ratio, and pH? (2016, methodology): “We used visreg (Breheny and Burchett 2013) and (Grapher 11 Golden Software LLC) to plot and visualize effects and relationships graphically.”
• Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects (2021, methodology): “Adjusted prediction plots have been available in leading statistical software for years (Breheny & Burchett, 2017; Fox, 2003; Williams, 2012), and adding a scatterplot of the data is not difficult to do (e.g., Mitchell, 2012b, p. 448).”