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 afterprocessMI
is run (including pseudo \(R^2\) measures)processMI.fit.mult.impute
: added processing ofanova
result fromfit.mult.impute(..., lrt=TRUE)
prmiInfo
: print (or html) inputation parameters on the result ofprocessMI(..., '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.