# rms v2.0-2

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## rms Package

Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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 binary or ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.

## Functions in rms

 Name Description anova.rms Analysis of Variance (Wald and F Statistics) bj Buckley-James Multiple Regression Model survfit.cph Cox Predicted Survival predict.lrm Predicted Values for Binary and Ordinal Logistic Models matinv Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator psm Parametric Survival Model rms rms Methods and Generic Functions survplot Plot Survival Curves and Hazard Functions fastbw Fast Backward Variable Selection Glm rms Version of glm Rq rms Package Interface to quantreg Package summary.rms Summary of Effects in Model groupkm Kaplan-Meier Estimates vs. a Continuous Variable print.cph.fit Print cph.fit residuals.lrm Residuals from a Logistic Regression Model Fit Gls Fit Linear Model Using Generalized Least Squares plot.Predict Plot Effects of Variables Estimated by a Regression Model Fit residuals.ols Residuals for ols ols Linear Model Estimation Using Ordinary Least Squares pphsm Parametric Proportional Hazards form of AFT Models datadist Distribution Summaries for Predictor Variables robcov Robust Covariance Matrix Estimates cr.setup Continuation Ratio Ordinal Logistic Setup Function Compose an S Function to Compute X beta from a Fit latexrms LaTeX Representation of a Fitted Model survest.psm Parametric Survival Estimates pentrace Trace AIC and BIC vs. Penalty plot.xmean.ordinaly Plot Mean X vs. Ordinal Y rms.trans rms Special Transformation Functions lrm.fit Logistic Model Fitter Predict Compute Predicted Values and Confidence Limits val.prob Validate Predicted Probabilities predab.resample Predictive Ability using Resampling validate.ols Validation of an Ordinary Linear Model rmsMisc Miscellaneous Design Attributes and Utility Functions cph Cox Proportional Hazards Model and Extensions validate.cph Validation of a Fitted Cox or Parametric Survival Model's Indexes of Fit print.cph Print cph Results validate Resampling Validation of a Fitted Model's Indexes of Fit contrast.rms General Contrasts of Regression Coefficients bplot 3-D Plots Showing Effects of Two Continuous Predictors in a Regression Model Fit survfit.formula Compute a Survival Curve for Censored Data validate.lrm Resampling Validation of a Logistic Model gendata Generate Data Frame with Predictor Combinations which.influence Which Observations are Influential ie.setup Intervening Event Setup bootcov Bootstrap Covariance and Distribution for Regression Coefficients val.surv Validate Predicted Probabilities Against Observed Survival Times rmsOverview Overview of rms Package lrm Logistic Regression Model validate.rpart Dxy and Mean Squared Error by Cross-validating a Tree Sequence hazard.ratio.plot Hazard Ratio Plot sensuc Sensitivity to Unmeasured Covariables survest.cph Cox Survival Estimates predictrms Predicted Values from Model Fit print.ols Print ols rms-internal Internal rms functions specs.rms rms Specifications for Models vif Variance Inflation Factors residuals.cph Residuals for a cph Fit calibrate Resampling Model Calibration nomogram Draw a Nomogram Representing a Regression Fit latex.cph LaTeX Representation of a Fitted Cox Model No Results!