similar to: Updates to rms package

I am cleaning up the rms package to not export functions not to be called directly by users. rms uses generic functions defined in other packages. For example there is a latex method in the Hmisc package, and rms has a latex method for objects of class "anova.rms" so there are anova.rms and latex.anova.rms functions in rms. I use:

A new version of rms is now available on CRAN for Linux and Windows (Mac will probably be available very soon). Largest changes include latex methods for validate.* and adding the capability to force a subset of variables to be included in all backwards stepdown models (single model or validation by resampling). Recent updates: * In survplot.rms, fixed bug (curves were undefined if

A new version of rms is now available on CRAN for Linux and Windows (Mac will probably be available very soon). Largest changes include latex methods for validate.* and adding the capability to force a subset of variables to be included in all backwards stepdown models (single model or validation by resampling). Recent updates: * In survplot.rms, fixed bug (curves were undefined if

Hi, Sorry not to provide R-code in my previous mail. R code is below #install.packages("rms") require(rms) #install.packages("mstate") library(mstate) require(splines) library(ggplot2) library(survival) library(splines) #install.packages("survsim") require(survsim) set.seed(10) df<-crisk.sim(n=500, foltime=10, dist.ev=rep("lnorm",2),

CRAN has a significant update to rms. Windows and unix/linux versions are available and I expect the Mac version to be available soon. The most significant improvement is addition of latex=TRUE arguments to model fitting print methods, made especially for use with Sweave. Here is a summary of changes since the previous version. Changes in version 3.1-0 (2010-09-12) * Fixed gIndex to not

CRAN has a significant update to rms. Windows and unix/linux versions are available and I expect the Mac version to be available soon. The most significant improvement is addition of latex=TRUE arguments to model fitting print methods, made especially for use with Sweave. Here is a summary of changes since the previous version. Changes in version 3.1-0 (2010-09-12) * Fixed gIndex to not

The rms ("Regression Modeling Strategies") package has undergone a massive update. The entire list of updates is at the bottom of this note. CRAN has the update for linux and will soon have it for Windows and Mac - check http://cran.r-project.org/web/packages/rms/ for availability. This rms update relies on a major update of the Hmisc package. The most user-visible changes are:

I have been attempting to use cph models to get better calibration of my models for which I had originally used logistic regression. I tried running with 40 repetitions and got an error. I then tried 500 repetitions (thinking that the NaNs in the output below might be caused by that choice) and then let my computer crunch for several hours and got only the same error message and

Version 2.2-0 of the rms package is now available. This is a somewhat major update. One major change is not downward compatible: Instead of specifying predictor=. or predictor=NA to Predict, summary, nomogram, survplot, gendata, you just specify the name of the predictor. For example, to get predictions for the default range of x1 and for just 2 values of x2 you might specify Predict(fit,

Version 2.2-0 of the rms package is now available. This is a somewhat major update. One major change is not downward compatible: Instead of specifying predictor=. or predictor=NA to Predict, summary, nomogram, survplot, gendata, you just specify the name of the predictor. For example, to get predictions for the default range of x1 and for just 2 values of x2 you might specify Predict(fit,

While sm4.6ll<-fit.mult.impute(Surv(agesi, si)~partner+ in.love+ pubty+ FPA+ strat(gender),fitter = cph, xtrans = dated.sexrisk2.i, data = dated.sexrisk2, x=T,y=T,surv=T, time.inc=16) runs perfectly using Hmisc, Design and mice under R11 run via Sciviews-K, with library(Design) library(mice) ds2d<-datadist(dated.sexrisk2) options(datadist="ds2d")

Hello! I have created a bplot-figure using this code: *file <- "2dcali_red.ttt" ux<-as.matrix(read.table(file, dec = ",")) mode(ux)<-'numeric' vel<-ux[,1] ang<-ux[,2] x<-ux[,3] y<-ux[,4] dat<- data.frame(ang=ang, x=x,y=y) require(rms) ddist2 <- datadist(dat) options(datadist="ddist2") fitn <- lrm(ang ~ rcs(x,4) +

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

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

Dear list, I'm facing the following problem : A cox model with my sex variable interacting with several continuous variables : cph(S~sex*(x1+x2+x3)) And I'd like to make a nomogram. I know it's a bit tricky and one mights argue that nomogram is not a good a choice... I could use the parameter interact=list(sex=("male","female"),x1=c(a,b,c))... but with rcs or pol

I have a plot produced by function bplot (package = rms) that is really a lattice plot (class="trellis"). It is similar to this plot produced by a very minor modification of the first example on the bplot help page: requiere(rms) n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120,

Hi R experts, I am trying to do a prognostic model validation study, using cancer survival data. There are 2 data sets - 1500 cases used to develop a nomogram, and another of 800 cases used as an independent validation cohort. I have validated the nomogram in the original data (easy with the Design tools), and then want to show that it also has good results with the independent data using 60

 Dear R-users,   I  have two questions regarding validation and calibration of Survival regression models.   1.  I am trying to calibrate and validate a cox model using val.surv. here is my code:  f.1<-cph(Surv(time,event)~age, x=T, y=T, data=train)  test1<-test[,"age"]  val.surv(f.1, newdata=data.frame(test1), u=10)    but I get an error message:    Error in val.surv(f.1, newdata

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

Dear R users: In Prof. Harrell's library rms, calibrate.rms plot the Bias-corrected Probability and Apparent Probability. The latter one can be retrieved from class calibrate.default. But how to retrieve the former one. BW *Yao Zhu* *Department of Urology Fudan University Shanghai Cancer Center Shanghai, China* [[alternative HTML version deleted]]