rms v6.1-0


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Regression Modeling Strategies

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.



Regression Modeling Strategies

Current Goals

  • Implement estimation and prediction methods for the Bayesian partial proportional odds model blrm function

Web Sites

To Do

  • Fix survplot so that explicitly named adjust-to values are still in subtitles. See tests/cph2.s.
  • Fix fit.mult.impute to average sigma^2 and then take square root, instead of averaging sigma
  • Implement user-added distributions in psm - see https://github.com/harrelfe/rms/issues/41

Functions in rms

Name Description
Glm rms Version of glm
ExProb Function Generator For Exceedance Probabilities
bootBCa BCa Bootstrap on Existing Bootstrap Replicates
Gls Fit Linear Model Using Generalized Least Squares
Predict Compute Predicted Values and Confidence Limits
Function Compose an S Function to Compute X beta from a Fit
Rq rms Package Interface to quantreg Package
anova.rms Analysis of Variance (Wald and F Statistics)
bootcov Bootstrap Covariance and Distribution for Regression Coefficients
bj Buckley-James Multiple Regression Model
gendata Generate Data Frame with Predictor Combinations
cph Cox Proportional Hazards Model and Extensions
contrast.rms General Contrasts of Regression Coefficients
bplot 3-D Plots Showing Effects of Two Continuous Predictors in a Regression Model Fit
calibrate Resampling Model Calibration
gIndex Calculate Total and Partial g-indexes for an rms Fit
fastbw Fast Backward Variable Selection
ggplot.Predict Plot Effects of Variables Estimated by a Regression Model Fit Using ggplot2
datadist Distribution Summaries for Predictor Variables
matinv Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator
ie.setup Intervening Event Setup
cr.setup Continuation Ratio Ordinal Logistic Setup
nomogram Draw a Nomogram Representing a Regression Fit
latex.cph LaTeX Representation of a Fitted Cox Model
hazard.ratio.plot Hazard Ratio Plot
groupkm Kaplan-Meier Estimates vs. a Continuous Variable
lrm Logistic Regression Model
lrm.fit.bare lrm.fit.bare
latexrms LaTeX Representation of a Fitted Model
lrm.fit Logistic Model Fitter
pentrace Trace AIC and BIC vs. Penalty
plotp.Predict Plot Effects of Variables Estimated by a Regression Model Fit Using plotly
poma Examine proportional odds and parallelism assumptions of `orm` and `lrm` model fits.
orm.fit Ordinal Regression Model Fitter
orm Ordinal Regression Model
npsurv Nonparametric Survival Estimates for Censored Data
ols Linear Model Estimation Using Ordinary Least Squares
plot.Predict Plot Effects of Variables Estimated by a Regression Model Fit
print.cph Print cph Results
print.Glm print.glm
plot.contrast.rms plot.contrast.rms
plot.xmean.ordinaly Plot Mean X vs. Ordinal Y
residuals.cph Residuals for a cph Fit
predict.lrm Predicted Values for Binary and Ordinal Logistic Models
predictrms Predicted Values from Model Fit
print.ols Print ols
pphsm Parametric Proportional Hazards form of AFT Models
sensuc Sensitivity to Unmeasured Covariables
psm Parametric Survival Model
residuals.lrm Residuals from an lrm or orm Fit
rms-internal Internal rms functions
predab.resample Predictive Ability using Resampling
residuals.ols Residuals for ols
robcov Robust Covariance Matrix Estimates
survfit.cph Cox Predicted Survival
rmsMisc Miscellaneous Design Attributes and Utility Functions
rms rms Methods and Generic Functions
rms.trans rms Special Transformation Functions
validate Resampling Validation of a Fitted Model's Indexes of Fit
validate.cph Validation of a Fitted Cox or Parametric Survival Model's Indexes of Fit
survplot Plot Survival Curves and Hazard Functions
specs.rms rms Specifications for Models
summary.rms Summary of Effects in Model
val.prob Validate Predicted Probabilities
validate.rpart Dxy and Mean Squared Error by Cross-validating a Tree Sequence
validate.ols Validation of an Ordinary Linear Model
val.surv Validate Predicted Probabilities Against Observed Survival Times
validate.Rq Validation of a Quantile Regression Model
setPb Progress Bar for Simulations
vif Variance Inflation Factors
survest.cph Cox Survival Estimates
survest.psm Parametric Survival Estimates
validate.lrm Resampling Validation of a Logistic or Ordinal Regression Model
rmsOverview Overview of rms Package
which.influence Which Observations are Influential
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