Regression Modeling Strategies
Resampling Model Calibration
General Contrasts of Regression Coefficients
Cox Proportional Hazards Model and Extensions
Continuation Ratio Ordinal Logistic Setup
Distribution Summaries for Predictor Variables
Logistic Regression Model
Recode Censored Ordinal Variable
Ocens2Surv
Likehood-Based Statistics for Other Links for orm Fits
Linear Model Estimation Using Ordinary Least Squares
ordESS
Check Parallelism Assumption of Ordinal Semiparametric Models
Ordinal Regression Model Fitter
Predicted Values for Binary and Ordinal Logistic Models
Robust Covariance Matrix Estimates
rms Package Interface to quantreg Package
Title survest.orm
Parametric Survival Estimates
Validation of a Quantile Regression Model
Variance Inflation Factors
Which Observations are Influential
Xcontrast
Overview of rms Package
is.na Method for Ocens Objects
LaTeX Representation of a Fitted Cox Model
LaTeX Representation of a Fitted Model
lrm.fit
Calculate Total and Partial g-indexes for an rms Fit
prmiInfo
Analysis of Variance (Wald, LR, and F Statistics)
Convert Ocens Object to Data Frame to Facilitate Subset
Buckley-James Multiple Regression Model
BCa Bootstrap on Existing Bootstrap Replicates
Bootstrap Covariance and Distribution for Regression Coefficients
3-D Plots Showing Effects of Two Continuous Predictors in a Regression...
Check Parallelism Assumption of Ordinal Semiparametric Models
Function Generators For Exceedance and Survival Probabilities
Fast Backward Variable Selection
Compose an S Function to Compute X beta from a Fit
Generate Data Frame with Predictor Combinations
Title Plot npsurv Nonparametric Survival Curves Using ggplot2
Plot Effects of Variables Estimated by a Regression Model Fit Using gg...
rms Version of glm
Fit Linear Model Using Generalized Least Squares
Kaplan-Meier Estimates vs. a Continuous Variable
Hazard Ratio Plot
Intervening Event Setup
Impact of Proportional Odds Assumpton
Exported Functions That Were Imported From Other Packages
Operate on Information Matrices
LRupdate
Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator
Draw a Nomogram Representing a Regression Fit
Nonparametric Survival Estimates for Censored Data
Censored Ordinal Variable
residuals.Glm
Ordinal Regression Model
Trace AIC and BIC vs. Penalty
plot.contrast.rms
Plot Effects of Variables Estimated by a Regression Model Fit
plot.rexVar
Plot Mean X vs. Ordinal Y
Plot Intercepts
Plot Effects of Variables Estimated by a Regression Model Fit Using pl...
Examine proportional odds and parallelism assumptions of orm and `lr...
Parametric Proportional Hazards form of AFT Models
Predictive Ability using Resampling
Dxy and Mean Squared Error by Cross-validating a Tree Sequence
Compute Predicted Values and Confidence Limits
Predicted Values from Model Fit
Print cph Results
print.glm
Print Result from impactPO
print Method for Ocens Objects
Print ols
print.rexVar
processMI.fit.mult.impute
processMI
Parametric Survival Model
Prepare units for Printing and Plotting
recode2integer
Residuals for a cph Fit
Residuals from an lrm or orm Fit
Residuals for ols
rexVar
Internal rms functions
rms Methods and Generic Functions
rms Special Transformation Functions
Miscellaneous Design Attributes and Utility Functions
Sensitivity to Unmeasured Covariables
Progress Bar for Simulations
rms Specifications for Models
Ocens
Summary of Effects in Model
Cox Survival Estimates
Cox Predicted Survival
Title Survival Curve Plotting
Plot Survival Curves and Hazard Functions
Validate Predicted Probabilities
Validate Predicted Probabilities Against Observed Survival Times
Validation of a Fitted Cox or Parametric Survival Model's Indexes of F...
Resampling Validation of a Logistic or Ordinal Regression Model
Validation of an Ordinary Linear Model
Resampling Validation of a Fitted Model's Indexes of Fit
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.