Learn R Programming

⚠️There's a newer version (6.8-2) of this package.Take me there.

rms (version 4.1-3)

Regression Modeling Strategies

Description

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.

Copy Link

Version

Install

install.packages('rms')

Monthly Downloads

27,322

Version

4.1-3

License

GPL (>= 2)

Last Published

March 2nd, 2014

Functions in rms (4.1-3)

Function

Compose an S Function to Compute X beta from a Fit
Predict

Compute Predicted Values and Confidence Limits
groupkm

Kaplan-Meier Estimates vs. a Continuous Variable
Rq

rms Package Interface to quantreg Package
plot.Predict

Plot Effects of Variables Estimated by a Regression Model Fit
anova.rms

Analysis of Variance (Wald and F Statistics)
ols

Linear Model Estimation Using Ordinary Least Squares
ie.setup

Intervening Event Setup
orm.fit

Ordinal Regression Model Fitter
cr.setup

Continuation Ratio Ordinal Logistic Setup
pentrace

Trace AIC and BIC vs. Penalty
bj

Buckley-James Multiple Regression Model
bplot

3-D Plots Showing Effects of Two Continuous Predictors in a Regression Model Fit
gIndex

Calculate Total and Partial g-indexes for an rms Fit
plot.xmean.ordinaly

Plot Mean X vs. Ordinal Y
bootBCa

BCa Bootstrap on Existing Bootstrap Replicates
gendata

Generate Data Frame with Predictor Combinations
Gls

Fit Linear Model Using Generalized Least Squares
ExProb

Function Generator For Exceedance Probabilities
Glm

rms Version of glm
setPb

Progress Bar for Simulations
nomogram

Draw a Nomogram Representing a Regression Fit
rms.trans

rms Special Transformation Functions
survest.cph

Cox Survival Estimates
psm

Parametric Survival Model
hazard.ratio.plot

Hazard Ratio Plot
sensuc

Sensitivity to Unmeasured Covariables
print.ols

Print ols
specs.rms

rms Specifications for Models
robcov

Robust Covariance Matrix Estimates
val.prob

Validate Predicted Probabilities
survest.psm

Parametric Survival Estimates
validate.Rq

Validation of a Quantile Regression Model
which.influence

Which Observations are Influential
pphsm

Parametric Proportional Hazards form of AFT Models
fastbw

Fast Backward Variable Selection
survplot

Plot Survival Curves and Hazard Functions
residuals.ols

Residuals for ols
summary.rms

Summary of Effects in Model
validate.cph

Validation of a Fitted Cox or Parametric Survival Model's Indexes of Fit
bootcov

Bootstrap Covariance and Distribution for Regression Coefficients
datadist

Distribution Summaries for Predictor Variables
validate.rpart

Dxy and Mean Squared Error by Cross-validating a Tree Sequence
rmsMisc

Miscellaneous Design Attributes and Utility Functions
latex.cph

LaTeX Representation of a Fitted Cox Model
survfit.cph

Cox Predicted Survival
lrm.fit

Logistic Model Fitter
print.cph

Print cph Results
predictrms

Predicted Values from Model Fit
rms-internal

Internal rms functions
orm

Ordinal Regression Model
validate

Resampling Validation of a Fitted Model's Indexes of Fit
lrm

Logistic Regression Model
validate.lrm

Resampling Validation of a Logistic or Ordinal Regression Model
cph

Cox Proportional Hazards Model and Extensions
matinv

Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator
residuals.lrm

Residuals from an lrm or orm Fit
predict.lrm

Predicted Values for Binary and Ordinal Logistic Models
vif

Variance Inflation Factors
survfit.formula

Compute a Survival Curve for Censored Data
calibrate

Resampling Model Calibration
rms

rms Methods and Generic Functions
latexrms

LaTeX Representation of a Fitted Model
val.surv

Validate Predicted Probabilities Against Observed Survival Times
rmsOverview

Overview of rms Package
contrast.rms

General Contrasts of Regression Coefficients
predab.resample

Predictive Ability using Resampling
residuals.cph

Residuals for a cph Fit
validate.ols

Validation of an Ordinary Linear Model