# rms v6.1-0

Monthly downloads

## 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.

## Readme

# rms

Regression Modeling Strategies

# Current Goals

- Implement estimation and prediction methods for the Bayesian partial
proportional odds model
`blrm`

function

# Web Sites

- Overall: http://hbiostat.org/R/rms/
- Book: http://hbiostat.org/rms/
- CRAN: http://cran.r-project.org/web/packages/rms/
- Changelog: https://github.com/harrelfe/rms/commits/master/

# 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 | |

Glm | rms Version of glm | |

ExProb | Function Generator For Exceedance Probabilities | |

bootBCa | BCa Bootstrap on Existing Bootstrap Replicates | |

Gls | Fit Linear Model Using Generalized Least Squares | |

Predict | Compute Predicted Values and Confidence Limits | |

Function | Compose an S Function to Compute X beta from a Fit | |

Rq | rms Package Interface to quantreg Package | |

anova.rms | Analysis of Variance (Wald and F Statistics) | |

bootcov | Bootstrap Covariance and Distribution for Regression Coefficients | |

bj | Buckley-James Multiple Regression Model | |

gendata | Generate Data Frame with Predictor Combinations | |

cph | Cox Proportional Hazards Model and Extensions | |

contrast.rms | General Contrasts of Regression Coefficients | |

bplot | 3-D Plots Showing Effects of Two Continuous Predictors in a Regression Model Fit | |

calibrate | Resampling Model Calibration | |

gIndex | Calculate Total and Partial g-indexes for an rms Fit | |

fastbw | Fast Backward Variable Selection | |

ggplot.Predict | Plot Effects of Variables Estimated by a Regression Model Fit Using ggplot2 | |

datadist | Distribution Summaries for Predictor Variables | |

matinv | Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator | |

ie.setup | Intervening Event Setup | |

cr.setup | Continuation Ratio Ordinal Logistic Setup | |

nomogram | Draw a Nomogram Representing a Regression Fit | |

latex.cph | LaTeX Representation of a Fitted Cox Model | |

hazard.ratio.plot | Hazard Ratio Plot | |

groupkm | Kaplan-Meier Estimates vs. a Continuous Variable | |

lrm | Logistic Regression Model | |

lrm.fit.bare | lrm.fit.bare | |

latexrms | LaTeX Representation of a Fitted Model | |

lrm.fit | Logistic Model Fitter | |

pentrace | Trace AIC and BIC vs. Penalty | |

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. | |

orm.fit | Ordinal Regression Model Fitter | |

orm | Ordinal Regression Model | |

npsurv | Nonparametric Survival Estimates for Censored Data | |

ols | Linear Model Estimation Using Ordinary Least Squares | |

plot.Predict | Plot Effects of Variables Estimated by a Regression Model Fit | |

print.cph | Print cph Results | |

print.Glm | print.glm | |

plot.contrast.rms | plot.contrast.rms | |

plot.xmean.ordinaly | Plot Mean X vs. Ordinal Y | |

residuals.cph | Residuals for a cph Fit | |

predict.lrm | Predicted Values for Binary and Ordinal Logistic Models | |

predictrms | Predicted Values from Model Fit | |

print.ols | Print ols | |

pphsm | Parametric Proportional Hazards form of AFT Models | |

sensuc | Sensitivity to Unmeasured Covariables | |

psm | Parametric Survival Model | |

residuals.lrm | Residuals from an lrm or orm Fit | |

rms-internal | Internal rms functions | |

predab.resample | Predictive Ability using Resampling | |

residuals.ols | Residuals for ols | |

robcov | Robust Covariance Matrix Estimates | |

survfit.cph | Cox Predicted Survival | |

rmsMisc | Miscellaneous Design Attributes and Utility Functions | |

rms | rms Methods and Generic Functions | |

rms.trans | rms Special Transformation Functions | |

validate | Resampling Validation of a Fitted Model's Indexes of Fit | |

validate.cph | Validation of a Fitted Cox or Parametric Survival Model's Indexes of Fit | |

survplot | Plot Survival Curves and Hazard Functions | |

specs.rms | rms Specifications for Models | |

summary.rms | Summary of Effects in Model | |

val.prob | Validate Predicted Probabilities | |

validate.rpart | Dxy and Mean Squared Error by Cross-validating a Tree Sequence | |

validate.ols | Validation of an Ordinary Linear Model | |

val.surv | Validate Predicted Probabilities Against Observed Survival Times | |

validate.Rq | Validation of a Quantile Regression Model | |

setPb | Progress Bar for Simulations | |

vif | Variance Inflation Factors | |

survest.cph | Cox Survival Estimates | |

survest.psm | Parametric Survival Estimates | |

validate.lrm | Resampling Validation of a Logistic or Ordinal Regression Model | |

rmsOverview | Overview of rms Package | |

which.influence | Which Observations are Influential | |

No Results! |

## Last month downloads

## Details

Date | 2020-11-28 |

License | GPL (>= 2) |

URL | https://hbiostat.org/R/rms/, https://github.com/harrelfe/rms, https://www.nicholas-ollberding.com/post/an-introduction-to-the-harrell-verse-predictive-modeling-using-the-hmisc-and-rms-packages, https://thomaselove.github.io/432-notes/linear-regression-and-the-smartcle1-data.html |

LazyLoad | yes |

RoxygenNote | 7.1.1 |

NeedsCompilation | yes |

Packaged | 2020-11-29 12:56:50 UTC; harrelfe |

Repository | CRAN |

Date/Publication | 2020-11-29 14:10:02 UTC |

suggests | boot , knitr , mice , plotly (>= 4.5.6) , rmsb , tcltk |

imports | cluster , digest , htmlTable (>= 1.11.0) , htmltools , MASS , methods , multcomp , nlme (>= 3.1-123) , polspline , quantreg , rpart |

depends | ggplot2 (>= 2.2) , Hmisc (>= 4.3-0) , lattice , R (>= 3.5.0) , SparseM , survival (>= 3.1-12) |

Contributors | Frank Harrell Jr |

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