Visualization of regression models using visreg

R Journal

Volumn 9, Issue 2, 2017, Pages 56-71

  Breheny, Patrick ^a   Burchett, Woodrow ^b  

^a UNIVERSITY OF IOWA   (United States)

^b UNIVERSITY OF KENTUCKY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85041177139     PISSN: None     EISSN: 20734859     Source Type: Journal    
DOI: 10.32614/rj-2017-046     Document Type: Article

References (37)

1. D. Adler and D. Murdoch. Rgl: 3D Visualization Device System (OpenGL), 2011. R package version 0.92.798
2. A. C. Atkinson. Regression diagnostics, transformations and constructed variables. Journal of the Royal Statistical Society B, 44:pp. 1-36, 1982
3. A. C. Atkinson. Plots, Transformations, and Regression. Oxford University Press, 1985
4. D. Bates, M. Maechler, and B. Bolker. Lme4: Linear Mixed-Effects Models Using S4 Classes, 2012. R package version 0.999999-0
5. D. A. Belsley, E. Kuh, and R. E.Welsch. Regression Diagnostics. John Wiley & Sons, 1980
6. L. Breiman, A. Cutler, A. Liaw, and M.Wiener. randomForest: Breiman and Cutler's Random Forests for Classification and Regression, 2015. R package version 4.6-12
7. R. D. Cook. Exploring partial residual plots. Technometrics, 35:351-362, 1993. URL https://doi.org/ 10.2307/1270269
8. R. D. Cook and S.Weisberg. Residuals and Influence in Regression. Chapman and Hall, 1982
9. P. J. Diggle, P. J. Heagerty, K.-Y. Liang, and S. L. Zeger. Analysis of Longitudinal Data. Oxford University Press, 2002
10. B. Efron. Better bootstrap confidence intervals. Journal of the American Statistical Association, 82:171-185, 1987. URL https://doi.org/10.2307/2289144
11. M. Ezekiel. A method of handling curvilinear correlation for any number of variables. Journal of the American Statistical Association, 19:431-453, 1924. URL https://doi.org/10.2307/2281561
12. J. Fox. Effect displays in R for generalised linear models. Journal of Statistical Software, 8:1-27, 2003. URL https://doi.org/10.18637/jss.v008.i15
13. J. Fox and S.Weisberg. An R Companion to Applied Regression. Sage, Thousand Oaks CA, 2nd edition, 2011
14. P. M. Grambsch and T. M. Therneau. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika, 81:pp. 515-526, 1994. URL https://doi.org/10.1093/biomet/81.3.515
15. F. Harrell. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer-Verlag, 2015
16. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, 2009
17. T. J. Hastie and R. J. Tibshirani. Generalized Additive Models. Chapman & Hall/CRC, 1990
18. D.W. Hosmer and S. Lemeshow. Applied Logistic Regression. John Wiley & Sons, 2000
19. P. E. Johnson. Rockchalk: Regression Estimation and Presentation, 2016. R package version 1.8.101
20. M. Kutner, C. Nachtsheim, J. Neter, and W. Li. Applied Linear Statistical Models. McGraw-Hill, 2004
21. J. M. Landwehr, D. Pregibon, and A. C. Shoemaker. Graphical methods for assessing logistic regression models. Journal of the American Statistical Association, 79:61-71, 1984. URL https://doi.org/10. 2307/2288334
22. W. A. Larsen and S. J. McCleary. The use of partial residual plots in regression analysis. Technometrics, 14:781-790, 1972. URL https://doi.org/10.2307/1267305
23. C. Loader. Locfit: Local Regression, Likelihood and Density Estimation., 2010. R package version 1.5-6
24. A. Loy and H. Hofmann. Diagnostic tools for hierarchical linear models. Wiley Interdisciplinary Reviews: Computational Statistics, 5:48-61, 2013. URL https://doi.org/10.1002/wics.1238
25. C. L. Mallows. Augmented partial residuals. Technometrics, 28:313-319, 1986. URL https://doi.org/ 10.2307/1268980
26. D. Meyer, E. Dimitriadou, K. Hornik, A. Weingessel, and F. Leisch. E1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien, 2017. R package version 1.6-8
27. R. J. O'Hara Hines and E. M. Carter. Improved added variable and partial residual plots for the detection of influential observations in generalized linear models. Journal of the Royal Statistical Society C, 42:pp. 3-20, 1993. URL https://doi.org/10.2307/2347405
28. D. Pregibon. Logistic regression diagnostics. The Annals of Statistics, 9:705-724, 1981. URL https: //doi.org/10.1214/aos/1176345513
29. G. Ridgeway. Gbm: Generalized Boosted Regression Models, 2017. R package version 2.1.3
30. D. Sarkar. Lattice: Multivariate Data Visualization with R. Springer-Verlag, 2008
31. T. Therneau. A Package for Survival Analysis in S, 2012. R package version 2.36-12
32. W. N. Venables and B. D. Ripley. Modern Applied Statistics with S. Springer-Verlag, 2002
33. H.Wickham. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, 2009
34. C. S. Withers and S. Nadarajah. Improved confidence regions based on edgeworth expansions. Computational Statistics & Data Analysis, 56:4366-4380, 2012. URL https://doi.org/10.1016/j. csda.2012.03.019
35. F. S.Wood. The use of individual effects and residuals in fitting equations to data. Technometrics, 15: 677-695, 1973. URL https://doi.org/10.2307/1267381
36. S. Wood. Mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML Smoothness Estimation, 2012. R package version 1.7-17
37. S. N. Wood. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC, 2006