Abstract
The methods described in this book are useful in any regression model that involves a linear combination of regression parameters. The software that is described below is useful in the same situations. Functions in R 520 allow interaction spline functions as well as a wide variety of predictor parameterizations for any regression function, and facilitate model validation by resampling.
R is the most comprehensive tool for general regression models for the following reasons.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
lrm and rcs are in the rms package.
References
C. F. Alzola and F. E. Harrell. An Introduction to S and the Hmisc and Design Libraries, 2006. Electronic book, 310 pages.
R. A. Becker, J. M. Chambers, and A. R. Wilks. The New S Language. Wadsworth and Brooks/Cole, Pacific Grove, CA, 1988.
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth and Brooks/Cole, Pacific Grove, CA, 1984.
J. M. Chambers and T. J. Hastie, editors. Statistical Models in S. Wadsworth and Brooks/Cole, Pacific Grove, CA, 1992.
W. S. Cleveland. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc, 74:829–836, 1979.
C. E. Davis, J. E. Hyde, S. I. Bangdiwala, and J. J. Nelson. An example of dependencies among variables in a conditional logistic regression. In S. H. Moolgavkar and R. L. Prentice, editors, Modern Statistical Methods in Chronic Disease Epi, pages 140–147. Wiley, New York, 1986.
J. H. Friedman. A variable span smoother. Technical Report 5, Laboratory for Computational Statistics, Department of Statistics, Stanford University, 1984.
F. E. Harrell. rms: R functions for biostatistical/epidemiologic modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit, 2013. Implements methods in Regression Modeling Strategies, New York:Springer, 2001.
F. E. Harrell and R. Goldstein. A survey of microcomputer survival analysis software: The need for an integrated framework. Am Statistician, 51:360–373, 1997.
T. Hastie and R. Tibshirani. Generalized Additive Models. Chapman and Hall, London, 1990.
R. Ihaka and R. Gentleman. R: A language for data analysis and graphics. J Comp Graph Stat, 5:299–314, 1996.
K. Imai, G. King, and O. Lau. Towards a common framework for statistical analysis and development. J Comp Graph Stat, 17(4):892–913, 2008.
R. Koenker. quantreg: Quantile Regression, 2009. R package version 4.38.
R. Koenker and G. Bassett. Regression quantiles. Econometrica, 46:33–50, 1978.
J. F. Lawless and K. Singhal. Efficient screening of nonnormal regression models. Biometrics, 34:318–327, 1978.
F. Leisch. Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis. In W. Härdle and B. Rönz, editors, Compstat 2002 — Proceedings in Computational Statistics, pages 575–580. Physica Verlag, Heidelberg, 2002. ISBN 3-7908-1517-9.
W. Original. survival: Survival analysis, including penalised likelihood, 2009. R package version 2.37-7.
M. J. Pencina, R. B. D’Agostino, and O. V. Demler. Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med, 31(2):101–113, 2012.
M. J. Pencina, R. B. D’Agostino, and E. W. Steyerberg. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med, 30:11–21, 2011.
M. J. Pencina, R. B. D’Agostino Sr, R. B. D’Agostino Jr, and R. S. Vasan. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat Med, 27:157–172, 2008.
J. C. Pinheiro and D. M. Bates. Mixed-Effects Models in S and S-PLUS. Springer, New York, 2000.
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013.
R. D. C. Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2015. ISBN 3-900051-07-0.
W. N. Venables and B. D. Ripley. Modern Applied Statistics with S. Springer-Verlag, New York, fourth edition, 2003.
Y. Wax. Collinearity diagnosis for a relative risk regression analysis: An application to assessment of diet-cancer relationship in epidemiological studies. Stat Med, 11:1273–1287, 1992.
Y. Xie. knitr: A general-purpose package for dynamic report generation in R, 2013. R package version 1.5.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Harrell, F.E. (2015). R Software. In: Regression Modeling Strategies. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-19425-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-19425-7_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19424-0
Online ISBN: 978-3-319-19425-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)