This is to announce a new package rms on CRAN. rms goes along with my
book Regression Modeling Strategies. The home page for rms is
http://biostat.mc.vanderbilt.edu/rms, or go directly to
http://biostat.mc.vanderbilt.edu/Rrms for information just about the
software.
rms is a re-write of the Design package that has improved graphics and
that duplicates very little code in the survival package. In
particular, rms does not use low-level C language interfaces to other
packages and will be easier to maintain. rms also interfaces to
quantile regression (new Rq function), and interfaces to glm and gls
have been renamed Glm and Gls. rms requires the latest version of Hmisc
on CRAN.
rms has cleaned up graphics routines to make them more modular, to use
lattice graphics, and to make it easier to use ggplot2 graphics.
Defaults for confidence bands are now gray scale-shaded polygons.
The most visable change for the user is the replacement of the
plot.Design function with the Predict, plot.Predict, and bplot
functions. plot.Predict is used for bivariate graphics (using lattice),
and bplot is used for 3-d graphics using base graphics functions image,
contour, and persp. Note that multi-panel lattice graphics are usually
better than 3-d graphics for showing the effects of multiple predictors
varying simultaneously. The output of Predict is suitable for direct use
by lattice (e.g., the xyplot function) and ggplot2 if you don't want to
use plot.Predict. plot.Predict allows you to specify a lattice formula
(less the left hand side) if you don't like plot.Predict's choice of
superpositioning and panel variables.
The following outlines the most significant change users will need to
make (the web page contains the complete list). Note that the
convention used for getting predictions over the default range is now
predictor=. rather than predictor=NA.
require(rms) # instead of Design; loads Hmisc and survival
plot(fit, x1=NA, x2=NA, ...) changed to
p <- Predict(fit, x1=., x2=., ...)
plot(p) # ?plot.Predict for details; produces a lattice object
plot(Predict(fit, ...))
print(plot(p)) # needed if using Sweave or are inside { }
plot(fit, .., method='image' or 'contour' or 'persp')
changed to
p <- Predict(..., np=50) # type ?Predict for details
bplot(p, method=) # ?bplot for details; uses base graphics
The nomogram function now has a plot method so nomogram() by itself does
not plot.
Type ?rmsOverview for an overview and extensive examples. The package's
home page contains a reference card you can print.
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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