rms v5.1-4


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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

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


Date 2019-11-16
License GPL (>= 2)
URL http://biostat.mc.vanderbilt.edu/rms
LazyLoad yes
NeedsCompilation yes
Packaged 2019-11-17 02:38:23 UTC; harrelfe
Repository CRAN
Date/Publication 2019-11-17 14:30:03 UTC

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