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rms (version 3.3-0)

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 229 functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models 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 logistic regression, 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.

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Version

Install

install.packages('rms')

Monthly Downloads

38,544

Version

3.3-0

License

GPL (>= 2)

Maintainer

Frank E Harrell Jr

Last Published

February 28th, 2011

Functions in rms (3.3-0)

Gls

Fit Linear Model Using Generalized Least Squares
nomogram

Draw a Nomogram Representing a Regression Fit
print.cph

Print cph Results
contrast.rms

General Contrasts of Regression Coefficients
validate

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

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

Parametric Survival Model
gendata

Generate Data Frame with Predictor Combinations
anova.rms

Analysis of Variance (Wald and F Statistics)
lrm.fit

Logistic Model Fitter
Predict

Compute Predicted Values and Confidence Limits
residuals.lrm

Residuals from a Logistic Regression Model Fit
latexrms

LaTeX Representation of a Fitted Model
pphsm

Parametric Proportional Hazards form of AFT Models
validate.lrm

Resampling Validation of a Logistic Model
survest.psm

Parametric Survival Estimates
latex.cph

LaTeX Representation of a Fitted Cox Model
predict.lrm

Predicted Values for Binary and Ordinal Logistic Models
Rq

rms Package Interface to quantreg Package
predab.resample

Predictive Ability using Resampling
gIndex

Calculate Total and Partial g-indexes for an rms Fit
validate.ols

Validation of an Ordinary Linear Model
rms.trans

rms Special Transformation Functions
survfit.cph

Cox Predicted Survival
cph

Cox Proportional Hazards Model and Extensions
ols

Linear Model Estimation Using Ordinary Least Squares
survplot

Plot Survival Curves and Hazard Functions
which.influence

Which Observations are Influential
hazard.ratio.plot

Hazard Ratio Plot
calibrate

Resampling Model Calibration
pentrace

Trace AIC and BIC vs. Penalty
plot.Predict

Plot Effects of Variables Estimated by a Regression Model Fit
rmsOverview

Overview of rms Package
bootcov

Bootstrap Covariance and Distribution for Regression Coefficients
lrm

Logistic Regression Model
rmsMisc

Miscellaneous Design Attributes and Utility Functions
val.prob

Validate Predicted Probabilities
residuals.cph

Residuals for a cph Fit
survfit.formula

Compute a Survival Curve for Censored Data
bplot

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

Variance Inflation Factors
robcov

Robust Covariance Matrix Estimates
plot.xmean.ordinaly

Plot Mean X vs. Ordinal Y
rms-internal

Internal rms functions
val.surv

Validate Predicted Probabilities Against Observed Survival Times
validate.rpart

Dxy and Mean Squared Error by Cross-validating a Tree Sequence
residuals.ols

Residuals for ols
rms

rms Methods and Generic Functions
datadist

Distribution Summaries for Predictor Variables
sensuc

Sensitivity to Unmeasured Covariables
cr.setup

Continuation Ratio Ordinal Logistic Setup
validate.cph

Validation of a Fitted Cox or Parametric Survival Model's Indexes of Fit
survest.cph

Cox Survival Estimates
groupkm

Kaplan-Meier Estimates vs. a Continuous Variable
fastbw

Fast Backward Variable Selection
ie.setup

Intervening Event Setup
print.ols

Print ols
bj

Buckley-James Multiple Regression Model
matinv

Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator
specs.rms

rms Specifications for Models
Glm

rms Version of glm
predictrms

Predicted Values from Model Fit
summary.rms

Summary of Effects in Model