The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Visualization of Regression Models Using visreg
Open accessAbstract
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 confidence 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.S. N. Wood.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980) · Wiley Series in Probability and Statistics · doi:10.1002/0471725153 · Cited by 7,297
Proportional hazards tests and diagnostics based on weighted residuals
GRAMBSCH, P. M., & THERNEAU, T. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.515 · Cited by 5,885 · cited 1×
“, 1980; Cook and Weisberg, 1982); indeed, many diagnostics are specific to the type of model (e.g., Pregibon, 1981; Grambsch and Therneau, 1994; Loy and Hofmann, 2013).”
Background
Cited by
Mordecai, E. A., Cohen, J. M., Evans, M. V., Gudapati, P., Johnson, L. R., Lippi, C. A., Miazgowicz, K., Murdock, C. C., Rohr, J. R., Ryan, S. J., et al. (2017) · PLOS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0005568 · Cited by 785
Finding Optimal Normalizing Transformations via bestNormalize
Peterson, R. (2021) · The R Journal · doi:10.32614/rj-2021-041 · Cited by 478
The psychological roots of intellectual humility: The role of intelligence and cognitive flexibility
Zmigrod, L., Zmigrod, S., Rentfrow, P. J., & Robbins, T. W. (2019) · Personality and Individual Differences · doi:10.1016/j.paid.2019.01.016 · Cited by 142
Combined effects of agrochemicals and ecosystem services on crop yield across Europe
Gagic, V., Kleijn, D., Báldi, A., Boros, G., Jørgensen, H. B., Elek, Z., Garratt, M. P. D., de Groot, G. A., Hedlund, K., Kovács‐Hostyánszki, A., et al. (2017) · Ecology Letters · doi:10.1111/ele.12850 · Cited by 113
Sbrana, A., Valente, T., Scacco, U., Bianchi, J., Silvestri, C., Palazzo, L., de Lucia, G. A., Valerani, C., Ardizzone, G., & Matiddi, M. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.114429 · Cited by 82
Metadata
- Authors
- Breheny, P., & Burchett, W.
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2017
- Volume
- 9
- Issue
- 2
- Pages
- 56
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W2560656592
- Cited by
- 1,089
- References
- 6
- FWCI
- 48.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Visualization of Regression Models Using visreg. https://app.cochat.ai/papers/10.32614/rj-2017-046
- The paper
- Breheny, P., & Burchett, W. (2017). Visualization of Regression Models Using visreg. The R Journal. https://doi.org/10.32614/rj-2017-046
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.