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.
|Author||Frank E Harrell Jr <email@example.com>|
|Date of publication||2017-01-01 23:44:17|
|Maintainer||Frank E Harrell Jr <firstname.lastname@example.org>|
|License||GPL (>= 2)|
anova.rms: Analysis of Variance (Wald and F Statistics)
bj: Buckley-James Multiple Regression Model
bootBCa: BCa Bootstrap on Existing Bootstrap Replicates
bootcov: Bootstrap Covariance and Distribution for Regression...
bplot: 3-D Plots Showing Effects of Two Continuous Predictors in a...
calibrate: Resampling Model Calibration
contrast: General Contrasts of Regression Coefficients
cph: Cox Proportional Hazards Model and Extensions
cr.setup: Continuation Ratio Ordinal Logistic Setup
datadist: Distribution Summaries for Predictor Variables
ExProb: Function Generator For Exceedance Probabilities
fastbw: Fast Backward Variable Selection
Function: Compose an S Function to Compute X beta from a Fit
gendata: Generate Data Frame with Predictor Combinations
ggplot.Predict: Plot Effects of Variables Estimated by a Regression Model Fit...
gIndex: Calculate Total and Partial g-indexes for an rms Fit
Glm: rms Version of glm
Gls: Fit Linear Model Using Generalized Least Squares
groupkm: Kaplan-Meier Estimates vs. a Continuous Variable
hazard.ratio.plot: Hazard Ratio Plot
ie.setup: Intervening Event Setup
latex.cph: LaTeX Representation of a Fitted Cox Model
latexrms: LaTeX Representation of a Fitted Model
lrm: Logistic Regression Model
lrm.fit: Logistic Model Fitter
matinv: Total and Partial Matrix Inversion using Gauss-Jordan Sweep...
nomogram: Draw a Nomogram Representing a Regression Fit
npsurv: Nonparametric Survival Estimates for Censored Data
ols: Linear Model Estimation Using Ordinary Least Squares
orm: Ordinal Regression Model
orm.fit: Ordinal Regression Model Fitter
pentrace: Trace AIC and BIC vs. Penalty
plotp.Predict: Plot Effects of Variables Estimated by a Regression Model Fit...
plot.Predict: Plot Effects of Variables Estimated by a Regression Model Fit
plot.xmean.ordinaly: Plot Mean X vs. Ordinal Y
pphsm: Parametric Proportional Hazards form of AFT Models
predab.resample: Predictive Ability using Resampling
Predict: Compute Predicted Values and Confidence Limits
predict.lrm: Predicted Values for Binary and Ordinal Logistic Models
predictrms: Predicted Values from Model Fit
print.cph: Print cph Results
print.ols: Print ols
psm: Parametric Survival Model
residuals.cph: Residuals for a cph Fit
residuals.lrm: Residuals from an 'lrm' or 'orm' Fit
residuals.ols: Residuals for ols
rms: rms Methods and Generic Functions
rms-internal: Internal rms functions
rmsMisc: Miscellaneous Design Attributes and Utility Functions
rms.trans: rms Special Transformation Functions
robcov: Robust Covariance Matrix Estimates
Rq: rms Package Interface to quantreg Package
sensuc: Sensitivity to Unmeasured Covariables
setPb: Progress Bar for Simulations
specs.rms: rms Specifications for Models
summary.rms: Summary of Effects in Model
survest.cph: Cox Survival Estimates
survest.psm: Parametric Survival Estimates
survfit.cph: Cox Predicted Survival
survplot: Plot Survival Curves and Hazard Functions
validate: Resampling Validation of a Fitted Model's Indexes of Fit
validate.cph: Validation of a Fitted Cox or Parametric Survival Model's...
validate.lrm: Resampling Validation of a Logistic or Ordinal Regression...
validate.ols: Validation of an Ordinary Linear Model
validate.rpart: Dxy and Mean Squared Error by Cross-validating a Tree...
validate.Rq: Validation of a Quantile Regression Model
val.prob: Validate Predicted Probabilities
val.surv: Validate Predicted Probabilities Against Observed Survival...
vif: Variance Inflation Factors
which.influence: Which Observations are Influential
zzzrmsOverview: Overview of rms Package