Regression Modeling Strategies
print.rexVar
prmiInfo
processMI.fit.mult.impute
processMI
Parametric Survival Model
Residuals for a cph Fit
residuals.Glm
Robust Covariance Matrix Estimates
Dxy and Mean Squared Error by Cross-validating a Tree Sequence
Validation of a Quantile Regression Model
Variance Inflation Factors
Which Observations are Influential
Xcontrast
Overview of rms Package
Compose an S Function to Compute X beta from a Fit
Intervening Event Setup
Impact of Proportional Odds Assumpton
Analysis of Variance (Wald, LR, and F Statistics)
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...
Resampling Model Calibration
General Contrasts of Regression Coefficients
Cox Proportional Hazards Model and Extensions
Continuation Ratio Ordinal Logistic Setup
Distribution Summaries for Predictor Variables
Function Generator For Exceedance Probabilities
Fast Backward Variable Selection
Print Result from impactPO
Generate Data Frame with Predictor Combinations
Plot Effects of Variables Estimated by a Regression Model Fit Using gg...
Calculate Total and Partial g-indexes for an rms Fit
rms Version of glm
Fit Linear Model Using Generalized Least Squares
Kaplan-Meier Estimates vs. a Continuous Variable
Hazard Ratio Plot
Exported Functions That Were Imported From Other Packages
LaTeX Representation of a Fitted Cox Model
LaTeX Representation of a Fitted Model
lrm.fit.bare
Logistic Model Fitter
Logistic Regression Model
LRupdate
Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator
rms Package Interface to quantreg Package
Draw a Nomogram Representing a Regression Fit
Nonparametric Survival Estimates for Censored Data
Linear Model Estimation Using Ordinary Least Squares
Ordinal Regression Model Fitter
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 Effects of Variables Estimated by a Regression Model Fit Using pl...
Examine proportional odds and parallelism assumptions of orm and `lr...
Print ols
Parametric Proportional Hazards form of AFT Models
Predictive Ability using Resampling
Predicted Values for Binary and Ordinal Logistic Models
Compute Predicted Values and Confidence Limits
Predicted Values from Model Fit
Print cph Results
print.glm
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
Summary of Effects in Model
Cox Survival Estimates
Parametric Survival Estimates
Validation of an Ordinary Linear Model
Cox Predicted Survival
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
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.