rms6.8-2 package

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.

Maintainer: Frank E Harrell Jr License: GPL (>= 2) Last published: 2024-08-23