rms v5.1-3.1

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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.

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rms

Regression Modeling Strategies

Current Goals

  • A non-downward compatible change will occur in the next release of the package
  • The survfit.formula function (seen by the user as just survfit) for obtaining nonparametric survival estimates will be replaced by the npsurv function
  • The purpose is to avoid conflicts with the survival package
  • survfit.coxph has a new id option that generalizes individual=TRUE; need to change survfit.cph and survest.cph to use that

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

Date 2019-01-27
License GPL (>= 2)
URL http://biostat.mc.vanderbilt.edu/rms
LazyLoad yes
NeedsCompilation yes
Packaged 2019-04-21 11:25:24 UTC; ripley
Repository CRAN
Date/Publication 2019-04-22 06:59:10 UTC

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