similar to: New package: rms

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,

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 major new version of the rms package is now on CRAN. The most user-visible changes are: - interactive plotly graphic methods for model fits. The best example of this is survplot for npsurv (Kaplan-Meier) estimates where the number of risk pop up as you hover over the curves, and you can click to bring up confidence bands for differences in survival curves - html methods for model fit

A major new version of the rms package is now on CRAN. The most user-visible changes are: - interactive plotly graphic methods for model fits. The best example of this is survplot for npsurv (Kaplan-Meier) estimates where the number of risk pop up as you hover over the curves, and you can click to bring up confidence bands for differences in survival curves - html methods for model fit

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

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

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

A new version of rms is now available on CRAN for Linux/UNIX. I expect Mac and Windows versions to be available in a day or so. This version works with and requires the newest version of Therneau's survival package. More information is at http://biostat.mc.vanderbilt.edu/Rrms Changes in version 3.2-0 (2011-02-14) * Changed to be compatible with survival 2.36-3 which is now required

A new version of rms is now available on CRAN for Linux/UNIX. I expect Mac and Windows versions to be available in a day or so. This version works with and requires the newest version of Therneau's survival package. More information is at http://biostat.mc.vanderbilt.edu/Rrms Changes in version 3.2-0 (2011-02-14) * Changed to be compatible with survival 2.36-3 which is now required

The rms package, a replacement for the Design package, has been updated on CRAN. The most major change is the addition of smooth calibration curves for externally (val.surv function) or internally (calibrate.cph, calibrate.psm) validating a survival model with right-censored data. The polspline package is used to estimate the survival probability at a fixed time point as a function of the

The rms package, a replacement for the Design package, has been updated on CRAN. The most major change is the addition of smooth calibration curves for externally (val.surv function) or internally (calibrate.cph, calibrate.psm) validating a survival model with right-censored data. The polspline package is used to estimate the survival probability at a fixed time point as a function of the

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) +

Hello, I am constructing a nomogram using cph and nomogram commands in Dr. Harrell's Design/RMS package. The HR that I obtain for dichotomous and categorical variables are identical to those that I obtain using STATA stcox. However, the inter-quartile HR I obtain for continuous variables is obviously different, since STATA gives me HR for each unit (year, centimeter, etc) like coxph would

Hello, I am using Dr. Harrell's rms package to make a nomogram. I was able to make a beautiful one. However, I want to change 5-year survival probability to 5-year failure probability. I couldn?t get hazard rate from Hazard(f1) because I used cph for the model. Here is my code: library(rms) f1 <- cph(Surv(retime,dfs) ~ age+her2+t_stage+n_stage+er+grade+cytcyt+Cyt_PCDK2 , data=data11,

Dear R-users, I have used the nomogram function from the rms package for a logistic regresison model made with lrm(). Everything works perfectly (r version 2.15.1 on a mac). My question is this: if my final model is not the one created by lrm, but I internally validated the model and 'shrunk' the regression coefficients and computed a new intercept, how can I build a nomogram using that

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,

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