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---+ Regression Modeling Strategies ---++ With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis ---+++ Major Changes Since The First Edition 1 Creation of a now mature R package, =rms=, that replaces and greatly extends the =Design= library used in the first edition 1 Conversion of all of the book's code to R 1 Conversion of the book source into =knitr= reproducible document 1 All code from the text is executable and is on the web site 1 Use of color graphics and use of the =ggplot2= graphics package 1 Scanned images were re-drawn 1 New text about problems with dichotomization of continuous variables and with classification (as opposed to prediction) 1 Expanded material on multiple imputation and predictive mean matching and emphasis on multiple imputation (using the Hmisc =aregImpute= function) instead of single imputation 1 Addition of redundancy analysis 1 Added a new section in Chapter 5 on bootstrap confidence intervals for rankings of predictors 1 Replacement of the U.S. presidential election data with analyses of a new diabetes dataset from NHANES using ordinal and quantile regression 1 More emphasis on semiparametric ordinal regression models for continuous Y, as direct competitors of ordinary multiple regression, with a detailed case study 1 A new chapter on generalized least squares for analysis of serial response data 1 The case study in imputation and data reduction was completely reworked and now focuses only on data reduction, with the addition of sparse principal components 1 More information about indexes of predictive accuracy 1 Augmentation of the chapter on maximum likelihood to include more flexible ways of testing contrasts as well as new methods for obtaining simultaneous confidence intervals 1 Binary logistic regression case study 1 was completely re-worked, now providing examples of model selection and model approximation accuracy 1 Single imputation was dropped from binary logistic case study 2 1 The case study in transform-both-sides regression modeling has been reworked using simulated data where true transformations are known, and a new example of the smearing estimator was added 1 Addition of 225 references, most of them published 2001-2014 1 New guidance on minimum sample sizes needed by some of the models 1 De-emphasis of bootstrap bumping for obtaining simultaneous confidence regions, in favor of a general multiplicity approach
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Topic revision: r1 - 27 May 2015,
FrankHarrell
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