# rms v5.1-3

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