R rms
Package
Regression Modeling Strategies
News
rms 6.8-0
has a non-downward-compatible change to the orm
function that improves how unique numeric values are determined for dependent variables. Previous versions could give different results on different hardward due to behavior of the R unique
function for floating point vectors. Now unique values are determined by the y.precison
argument which defaults to multiplying values by \(10^5\) before rounding. Details are in this report by Shawn Garbett of the Vanderbilt Department of Biostatistics.
Version 6.8-0 also has an important new function for relative explained variation, rexVar
.
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.