# rms v5.1-3.1

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## Regression Modeling Strategies

Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, 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 regression models, 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.

# rms

Regression Modeling Strategies

# Current Goals

• A non-downward compatible change will occur in the next release of the package
• The survfit.formula function (seen by the user as just survfit) for obtaining nonparametric survival estimates will be replaced by the npsurv function
• The purpose is to avoid conflicts with the survival package
• survfit.coxph has a new id option that generalizes individual=TRUE; need to change survfit.cph and survest.cph to use that

# To Do

• Fix survplot so that explicitly named adjust-to values are still in subtitles. See tests/cph2.s.
• Fix fit.mult.impute to average sigma^2 and then take square root, instead of averaging sigma
• Implement user-added distributions in psm - see https://github.com/harrelfe/rms/issues/41

## Functions in rms

 Name Description contrast.rms General Contrasts of Regression Coefficients groupkm Kaplan-Meier Estimates vs. a Continuous Variable hazard.ratio.plot Hazard Ratio Plot plotp.Predict Plot Effects of Variables Estimated by a Regression Model Fit Using plotly pphsm Parametric Proportional Hazards form of AFT Models residuals.ols Residuals for ols rms-internal Internal rms functions survest.cph Cox Survival Estimates survest.psm Parametric Survival Estimates vif Variance Inflation Factors which.influence Which Observations are Influential Glm rms Version of glm Gls Fit Linear Model Using Generalized Least Squares gendata Generate Data Frame with Predictor Combinations ggplot.Predict Plot Effects of Variables Estimated by a Regression Model Fit Using ggplot2 latexrms LaTeX Representation of a Fitted Model lrm Logistic Regression Model predab.resample Predictive Ability using Resampling predict.lrm Predicted Values for Binary and Ordinal Logistic Models residuals.cph Residuals for a cph Fit ExProb Function Generator For Exceedance Probabilities Function Compose an S Function to Compute X beta from a Fit residuals.lrm Residuals from an lrm or orm Fit bplot 3-D Plots Showing Effects of Two Continuous Predictors in a Regression Model Fit calibrate Resampling Model Calibration rms rms Methods and Generic Functions rms.trans rms Special Transformation Functions validate Resampling Validation of a Fitted Model's Indexes of Fit validate.Rq Validation of a Quantile Regression Model sensuc Sensitivity to Unmeasured Covariables setPb Progress Bar for Simulations val.prob Validate Predicted Probabilities val.surv Validate Predicted Probabilities Against Observed Survival Times Predict Compute Predicted Values and Confidence Limits Rq rms Package Interface to quantreg Package nomogram Draw a Nomogram Representing a Regression Fit npsurv Nonparametric Survival Estimates for Censored Data ols Linear Model Estimation Using Ordinary Least Squares orm Ordinal Regression Model survfit.cph Cox Predicted Survival survplot Plot Survival Curves and Hazard Functions validate.ols Validation of an Ordinary Linear Model validate.rpart Dxy and Mean Squared Error by Cross-validating a Tree Sequence rmsOverview Overview of rms Package anova.rms Analysis of Variance (Wald and F Statistics) bj Buckley-James Multiple Regression Model cr.setup Continuation Ratio Ordinal Logistic Setup datadist Distribution Summaries for Predictor Variables ie.setup Intervening Event Setup latex.cph LaTeX Representation of a Fitted Cox Model orm.fit Ordinal Regression Model Fitter pentrace Trace AIC and BIC vs. Penalty predictrms Predicted Values from Model Fit print.cph Print cph Results specs.rms rms Specifications for Models summary.rms Summary of Effects in Model bootBCa BCa Bootstrap on Existing Bootstrap Replicates bootcov Bootstrap Covariance and Distribution for Regression Coefficients fastbw Fast Backward Variable Selection gIndex Calculate Total and Partial g-indexes for an rms Fit lrm.fit Logistic Model Fitter matinv Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator plot.Predict Plot Effects of Variables Estimated by a Regression Model Fit plot.xmean.ordinaly Plot Mean X vs. Ordinal Y print.ols Print ols psm Parametric Survival Model rmsMisc Miscellaneous Design Attributes and Utility Functions robcov Robust Covariance Matrix Estimates validate.cph Validation of a Fitted Cox or Parametric Survival Model's Indexes of Fit validate.lrm Resampling Validation of a Logistic or Ordinal Regression Model cph Cox Proportional Hazards Model and Extensions No Results!