rms (version 2.0-2)

rmsOverview: Overview of rms Package

Description

rms is the package that goes along with the book Regression Modeling Strategies. rms does regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a re-written version of the Design package that has improved graphics and duplicates very little code in the survival package.

The package is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains functions for binary and ordinal logistic regression models and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. rms works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized lease squares for longitudinal data (using the nlme package), generalized linear models, and quantile regression (using the quantreg package). rms requires the Hmisc package to be installed. Note that Hmisc has several functions useful for data analysis (especially data reduction and imputation).

Older references below pertaining to the Design package are relevant to rms.

Statistical Methods Implemented

• Ordinary linear regression models
• Binary and ordinal logistic models (proportional odds and continuation ratio models)
• Cox model
• Parametric survival models in the accelerated failure time class
• Buckley-James least-squares linear regression model with possibly right-censored responses
• Generalized linear model
• Quantile regression
• Generalized least squares
• Bootstrap model validation to obtain unbiased estimates of model performance without requiring a separate validation sample
• Automatic Wald tests of all effects in the model that are not parameterization-dependent (e.g., tests of nonlinearity of main effects when the variable does not interact with other variables, tests of nonlinearity of interaction effects, tests for whether a predictor is important, either as a main effect or as an effect modifier)
• Graphical depictions of model estimates (effect plots, odds/hazard ratio plots, nomograms that allow model predictions to be obtained manually even when there are nonlinear effects and interactions in the model)
• Various smoothed residual plots, including some new residual plots for verifying ordinal logistic model assumptions
• Composing S functions to evaluate the linear predictor ($X\hat{beta}$), hazard function, survival function, quantile functions analytically from the fitted model
• Typesetting of fitted model using LaTeX
• Robust covariance matrix estimation (Huber or bootstrap)
• Cubic regression splines with linear tail restrictions (natural splines)
• Tensor splines
• Interactions restricted to not be doubly nonlinear
• Penalized maximum likelihood estimation for ordinary linear regression and logistic regression models. Different parts of the model may be penalized by different amounts, e.g., you may want to penalize interaction or nonlinear effects more than main effects or linear effects
• Estimation of hazard or odds ratios in presence of nolinearity and interaction
• Sensitivity analysis for an unmeasured binary confounder in a binary logistic model