# R `rms`

Package

# Regression Modeling Strategies

## News

`rms 6.7-0`

appeared on CRAN 2023-05-08 and represents a major update. The most significant new feature is automatically computing all likelihood ratio (LR) \(\chi^2\) chunk test statistics that can be inferred from the model design when the model is fitted using `lrm, orm, psm, cph, Glm`

. I’ve been meaning to do this for more than 10 years because LR tests are more accurate than the default `anova.rms`

Wald tests. LR tests do not suffer from the Hauck-Donner effect when a predictor has an infinite regression coefficient that drives the Wald \(\chi^2\) to zero because the standard error blows up.

An example of a full LR `anova`

is here.

Also new is the implementation of LR tests when doing multiple imputation, using the method of Chan and Meng. This uses a new feature in `Hmisc:fit.mult.impute`

where besides testing on individual completed datasets the log likelihood is computed from a stacked dataset of all completed datasets. Specifying `lrt=TRUE`

to `fit.mult.impute`

will take the necessary actions to get LR tests with `processMI`

including setting argument `method`

to `'stack'`

which makes final regression coefficient estimates come from a single fit of a stacked dataset.

There are new `rms`

functions or options relating to this:

`LRupdate`

: update LR test-related stats after`processMI`

is run (including pseudo \(R^2\) measures)`processMI.fit.mult.impute`

: added processing of`anova`

result from`fit.mult.impute(..., lrt=TRUE)`

`prmiInfo`

: print (or html) inputation parameters on the result of`processMI(..., 'anova')`

This new `rms`

requires installing the latest `Hmisc`

from CRAN.

## Documentation | CRAN | GitHub | Online

- Examples in an R markdown/knitr html report
- Vignette for general multiparameter transformations using the
`gTrans`

function - Vignettes for Bayesian modeling with rmsb
- An Introduction to the Harrellverse by Nicholas Ollberding
- Linear Regression Case Study by Thomas Love
- Markov models for longitudinal data, here, here, and here
- Many test scripts
- Video demonstrating
`survplotp`

interactive survival curves - Online help with examples
- Changelog and News
- Package overview
- Manual
- Latest Linux source package
- To install: Download and
`sudo R CMD INSTALL rms-linux.tar.gz`

- To install: Download and
- Latest binary packages for Linux, Windows, and Mac arm64
- Notes about R^2 measures

## Evolution

`rms`

is an R package that is a replacement for the `Design`

package. The package accompanies FE Harrell’s book *Regression Modeling Strategies*. It began in 1991 as the S-Plus `Design`

package.

## Bug Reports

Please use `Issues`

on GitHub.