rms version 3.3-1 has been installed on CRAN. New features/bug fixes are
below.
* Added new example for anova.rms for making dot plots of partial R^2
of predictors
* Defined logLik.ols (calls logLik.lm)
* Fixed and cleaned up logLik.rms, AIC.rms
* Fixed residuals.psm to allow other type= values used by
residuals.survreg
* Fixed Predict and survplot.rms to allow for case where no covariates
present
* Fixed bug in val.prob where Eavg wasn't being defined if pl=FALSE
(thanks: Ben Haller)
* Fixed bug in Predict so that it could get a list or vector from
predictrms
* Fixed latex.rms to not treat * as a wild card in various contexts (may
be interaction)
* Fixed predictrms to temporarily get std.err if conf.int requested even
it std.err not; omitted std.err in returned object if not wanted
* Enhanced plot.Predict to allow plots for different predictors to be
combined, after running rbind.Predict (varypred argument)
* Also enhanced to allow groups= and cond= when varying the predictors
* Corrected bug where sometimes would try to plot confidence limits when
conf.int=FALSE was given to Predict
* Added india, indnl arguments to anova.rms to suppress printing
individual tests of interaction/nonlinearity
* Changed anova.rms so that if all non-summary terms have (Factor+Higher
Order Factor) in their labels, this part of the labels is suppressed (useful
with india and indnl)
Description: Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model
design
attributes in the fit. rms is a collection of 229 functions that
assist with and streamline modeling. It also contains functions
for
binary and ordinal logistic regression models 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 logistic
regression, 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.
More details are at http://biostat.mc.vanderbilt.edu/Rrms
-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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