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. 2024 Feb 2;19(2):e0297033.
doi: 10.1371/journal.pone.0297033. eCollection 2024.

Say farewell to bland regression reporting: Three forest plot variations for visualizing linear models

Jonathan Fries  1 , Sandra Oberleiter  1 , Jakob Pietschnig  1
Affiliations

Say farewell to bland regression reporting: Three forest plot variations for visualizing linear models

Jonathan Fries et al. PLoS One. .

Abstract

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Example scatter plot.
The red line represents the slope of a linear regression model with sepal length (of various Iris specimens) as outcome variable and petal length as predictor. This plot can be reproduced using the R code provided in the supplementary materials (S3 File).
Fig 2
Fig 2. Forest plot for a subset of the Mozart effect meta-analysis included in the metaviz R package [18, 19].
This plot can be reproduced using the R code provided in the supplementary materials (S3 File).
Fig 3
Fig 3. Forest plot of a multiple linear regression model of iris sepal length predicted by sepal width, petal length, and petal width.
Blue or red circles represent standardized predictor estimates (i.e., beta weights). Horizontal bars represent the corresponding CIs. The color of the predictor indicators changes according to the estimate’s sign. This plot can be reproduced using the R code provided in the supplementary materials (S3 File).
Fig 4
Fig 4. Beta-range forest plot for TIMSS data.
Fig 5
Fig 5. Bootstrap ridgeline plot, displaying miniature density plots of 500 parameter estimates for each decathlon regression model.
Fig 6
Fig 6. Bootstrap violin plot, displaying symmetrically mirrored density plots of 500 parameter estimates for each decathlon regression model.
See this image and copyright information in PMC

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