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