Package index • rms

All functions
`ExProb()` `plot(<ExProb>)` `Survival(<orm>)`	Function Generators For Exceedance and Survival Probabilities
`Function(<rms>)` `Function(<cph>)` `sascode()` `perlcode()`	Compose an S Function to Compute X beta from a Fit
`Glm()`	rms Version of glm
`Gls()` `print(<Gls>)`	Fit Linear Model Using Generalized Least Squares
`LRupdate()`	LRupdate
`Ocens()`	Censored Ordinal Variable
`Ocens2Surv()`	Ocens2Surv
`Ocens2ord()`	Recode Censored Ordinal Variable
`Olinks()`	Likehood-Based Statistics for Other Links for orm Fits
`Predict()` `print(<Predict>)` `rbind(<Predict>)`	Compute Predicted Values and Confidence Limits
`Punits()`	Prepare units for Printing and Plotting
`Rq()` `print(<Rq>)` `latex(<Rq>)` `predict(<Rq>)` `RqFit()`	rms Package Interface to quantreg Package
`Xcontrast()`	Xcontrast
`adapt_orm()`	Adaptive orm Fit For a Single Continuous Predictor
`anova(<rms>)` `print(<anova.rms>)` `plot(<anova.rms>)` `latex(<anova.rms>)` `html(<anova.rms>)`	Analysis of Variance (Wald, LR, and F Statistics)
`as.data.frame(<Ocens>)`	Convert `Ocens` Object to Data Frame to Facilitate Subset
`bj()` `print(<bj>)` `residuals(<bj>)` `bjplot()` `validate(<bj>)` `bj.fit()`	Buckley-James Multiple Regression Model
`bootBCa()`	BCa Bootstrap on Existing Bootstrap Replicates
`bootcov()` `bootplot()` `confplot()` `histdensity()`	Bootstrap Covariance and Distribution for Regression Coefficients
`bplot()` `perimeter()`	3-D Plots Showing Effects of Two Continuous Predictors in a Regression Model Fit
`calibrate()` `print(<calibrate>)` `print(<calibrate.default>)` `plot(<calibrate>)` `plot(<calibrate.default>)`	Resampling Model Calibration
`contrast()` `print(<contrast.rms>)`	General Contrasts of Regression Coefficients
`cph()` `Survival(<cph>)` `Quantile(<cph>)` `Mean(<cph>)`	Cox Proportional Hazards Model and Extensions
`cr.setup()`	Continuation Ratio Ordinal Logistic Setup
`datadist()` `print(<datadist>)`	Distribution Summaries for Predictor Variables
`fastbw()` `print(<fastbw>)`	Fast Backward Variable Selection
`gIndex()` `print(<gIndex>)` `plot(<gIndex>)`	Calculate Total and Partial g-indexes for an rms Fit
`gendata()`	Generate Data Frame with Predictor Combinations
`ggplot(<Predict>)`	Plot Effects of Variables Estimated by a Regression Model Fit Using ggplot2
`ggplot(<npsurv>)`	Title Plot npsurv Nonparametric Survival Curves Using ggplot2
`groupkm()`	Kaplan-Meier Estimates vs. a Continuous Variable
`hazard.ratio.plot()`	Hazard Ratio Plot
`ie.setup()`	Intervening Event Setup
`impactPO()`	Impact of Proportional Odds Assumpton
`Surv()` `ggplot()`	Exported Functions That Were Imported From Other Packages
`infoMxop()`	Operate on Information Matrices
`intCalibration()`	Check Parallelism Assumption of Ordinal Semiparametric Models
`is.na(<Ocens>)`	is.na Method for Ocens Objects
`latex(<cph>)` `latex(<lrm>)` `latex(<ols>)` `latex(<orm>)` `latex(<pphsm>)` `latex(<psm>)`	LaTeX Representation of a Fitted Cox Model
`latexrms()`	LaTeX Representation of a Fitted Model
`lrm()` `print(<lrm>)`	Logistic Regression Model
`lrm.fit()`	lrm.fit
`matinv()`	Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator
`nomogram()` `print(<nomogram>)` `plot(<nomogram>)` `legend.nomabbrev()`	Draw a Nomogram Representing a Regression Fit
`npsurv()`	Nonparametric Survival Estimates for Censored Data
`ols()`	Linear Model Estimation Using Ordinary Least Squares
`ordESS()`	ordESS
`ordParallel()`	Check Parallelism Assumption of Ordinal Semiparametric Models
`orm()` `print(<orm>)` `Quantile(<orm>)`	Ordinal Regression Model
`orm.fit()`	Ordinal Regression Model Fitter
`pentrace()` `effective.df()` `print(<pentrace>)` `plot(<pentrace>)`	Trace AIC and BIC vs. Penalty
`plot(<Predict>)` `pantext()`	Plot Effects of Variables Estimated by a Regression Model Fit
`plot(<contrast.rms>)`	plot.contrast.rms
`plot(<rexVar>)`	plot.rexVar
`plot(<xmean.ordinaly>)`	Plot Mean X vs. Ordinal Y
`plotIntercepts()`	Plot Intercepts
`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.
`pphsm()` `print(<pphsm>)` `vcov(<pphsm>)`	Parametric Proportional Hazards form of AFT Models
`predab.resample()`	Predictive Ability using Resampling
`predict(<lrm>)` `predict(<orm>)` `Mean(<lrm>)` `Mean(<orm>)`	Predicted Values for Binary and Ordinal Logistic Models
`predictrms()` `predict(<bj>)` `predict(<cph>)` `predict(<Glm>)` `predict(<Gls>)` `predict(<ols>)` `predict(<psm>)`	Predicted Values from Model Fit
`print(<Glm>)`	print.glm
`print(<Ocens>)`	print Method for Ocens Objects
`print(<cph>)`	Print cph Results
`print(<impactPO>)`	Print Result from impactPO
`print(<ols>)`	Print ols
`print(<rexVar>)`	print.rexVar
`prmiInfo()`	prmiInfo
`processMI()`	processMI
`processMI(<fit.mult.impute>)`	processMI.fit.mult.impute
`psm()` `print(<psm>)` `Hazard()` `Survival()` `Quantile(<psm>)` `Mean(<psm>)` `residuals(<psm>)` `survplot(<residuals.psm.censored.normalized>)` `lines(<residuals.psm.censored.normalized>)`	Parametric Survival Model
`recode2integer()`	recode2integer
`residuals(<Glm>)`	residuals.Glm
`residuals(<cph>)`	Residuals for a cph Fit
`residuals(<lrm>)` `residuals(<orm>)` `plot(<lrm.partial>)`	Residuals from an `lrm` or `orm` Fit
`residuals(<ols>)`	Residuals for ols
`rexVar()`	rexVar
`modelData()` `Design()`	rms Methods and Generic Functions
`asis()` `matrx()` `pol()` `lsp()` `rcs()` `catg()` `scored()` `strat()` `gTrans()` `%ia%` `makepredictcall(<rms>)`	rms Special Transformation Functions
`vcov(<rms>)` `vcov(<cph>)` `vcov(<Glm>)` `vcov(<Gls>)` `vcov(<lrm>)` `vcov(<ols>)` `vcov(<orm>)` `vcov(<psm>)` `DesignAssign()` `oos.loglik()` `Getlim()` `Getlimi()` `related.predictors()` `interactions.containing()` `combineRelatedPredictors()` `param.order()` `Penalty.matrix()` `Penalty.setup()` `logLik(<Gls>)` `logLik(<ols>)` `logLik(<rms>)` `AIC(<rms>)` `nobs(<rms>)` `lrtest()` `print(<lrtest>)` `univarLR()` `Newlabels()` `Newlevels()` `prModFit()` `prStats()` `reListclean()` `formatNP()` `latex(<naprint.delete>)` `html(<naprint.delete>)` `removeFormulaTerms()`	Miscellaneous Design Attributes and Utility Functions
`robcov()`	Robust Covariance Matrix Estimates
`sensuc()` `plot(<sensuc>)`	Sensitivity to Unmeasured Covariables
`setPb()`	Progress Bar for Simulations
`specs()` `print(<specs.rms>)`	rms Specifications for Models
`[`(<Ocens>)	Ocens
`summary(<rms>)` `print(<summary.rms>)` `latex(<summary.rms>)` `html(<summary.rms>)` `plot(<summary.rms>)`	Summary of Effects in Model
`survest()`	Cox Survival Estimates
`survest(<orm>)`	Title survest.orm
`survest(<psm>)` `print(<survest.psm>)`	Parametric Survival Estimates
`survfit(<cph>)`	Cox Predicted Survival
`survplot()` `survplotp()` `survdiffplot()`	Plot Survival Curves and Hazard Functions
`survplot(<orm>)`	Title Survival Curve Plotting
`val.prob()` `print(<val.prob>)` `plot(<val.prob>)`	Validate Predicted Probabilities
`val.surv()` `print(<val.survh>)` `plot(<val.survh>)` `plot(<val.surv>)`	Validate Predicted Probabilities Against Observed Survival Times
`validate()` `print(<validate>)` `latex(<validate>)` `html(<validate>)`	Resampling Validation of a Fitted Model's Indexes of Fit
`validate(<Rq>)`	Validation of a Quantile Regression Model
`validate(<cph>)` `validate(<psm>)` `dxy.cens()`	Validation of a Fitted Cox or Parametric Survival Model's Indexes of Fit
`validate(<lrm>)` `validate(<orm>)`	Resampling Validation of a Logistic or Ordinal Regression Model
`validate(<ols>)`	Validation of an Ordinary Linear Model
`validate(<rpart>)` `print(<validate.rpart>)` `plot(<validate.rpart>)`	Dxy and Mean Squared Error by Cross-validating a Tree Sequence
`vif()`	Variance Inflation Factors
`which.influence()` `show.influence()`	Which Observations are Influential
`rmsOverview` `rms.Overview`	Overview of rms Package

Reference

All functions