The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals
Open accessAbstract
Predictor effect displays, introduced in this article, visualize the response surface of complex regression models by averaging and conditioning, producing a sequence of 2D line graphs, one graph or set of graphs for each predictor in the regression problem. Partial residual plots visualize lack of fit, traditionally in relatively simple additive regression models. We combine partial residuals with effect displays to visualize both fit and lack of fit simultaneously in complex regression models, plotting residuals from a model around 2D slices of the fitted response surface. Employing fundamental results on partial residual plots along with examples for both real and contrived data, we discuss and illustrate both the strengths and limitations of the resulting graphs. The methods described in this paper are implemented in the effects package for R.
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
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2014.907095 · Cited by 1,861
Cited by
van Dijk, M., Morley, T., Rau, M. L., & Saghai, Y. (2021) · Nature Food · doi:10.1038/s43016-021-00322-9 · Cited by 2,339
Teixeira-Santos, A. C., Moreira, C. S., Pereira, D. R., Pinal, D., Fregni, F., Leite, J., Carvalho, S., & Sampaio, A. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.827188 · Cited by 27
Kombate, G., Gmakouba, W., Scott, S., Azianu, K. A., Ekouevi, D. K., & van der Sande, M. A. B. (2022) · Malaria Journal · doi:10.1186/s12936-022-04195-6 · Cited by 20
Arboreal ant abundance tracks primary productivity in an Amazonian whitewater river system
Pringle, E. G., Santos, T. F. D., Gonçalves, M. S., Hawes, J. E., Peres, C. A., & Baccaro, F. B. (2019) · Ecosphere · doi:10.1002/ecs2.2902 · Cited by 19
Scimeca, M., Peñaloza, C., & Kiran, S. (2024) · Bilingualism: Language and Cognition · doi:10.1017/s1366728923000391 · Cited by 11
Antoł, A., Rojek, W., Singh, S., Piekarski, D., & Czarnoleski, M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0220647 · Cited by 12
Antoł, A., Labecka, A. M., Horváthová, T., Sikorska, A., Szabla, N., Bauchinger, U., Kozłowski, J., & Czarnoleski, M. (2020) · Ecology and Evolution · doi:10.1002/ece3.6683 · Cited by 10
Metadata
- Authors
- Fox, J., & Weisberg, S.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2018
- Volume
- 87
- Issue
- 9
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2902127843
- Cited by
- 579
- References
- 3
- FWCI
- 27.16
- Type
- journal article
- Language
- en
- Open access
- diamond
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
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.
