similar to: survplot invert number at risk labels

Dear all, I am trying to plot KM curves with confidence bands with function survplot under package rms. However, the following codes do not seem to work. The KM curves are produced, but the confidence bands are not there. Any insights? Thanks in advance. library(rms) ########data generation############ n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age"

Dear R-help How can one get survplot() to place the number at risk just below the survival curve as opposed to the default which is just above the x-axis? I tried the code bellow but the result is not satisfactory as some numbers are repeated several times at different y coordinates and the position of the n.risk numbers corresponds to the x-axis tick marks not the survival curve time of

Hi I am a new fan of R after getting mad with the graphical functional in SPSS. I have been able to create a nice looking Kaplan Meyer graph using Survplot function. However I have difficulties in turning the y axis to percent instead of the default 0-1 scale. Further I have tried the function yaxt="n" without any results. Any help in this matter will be appreciated. The code is

Dear R-helpers I am wondering if there is an option to the survplot function in the design package that allows for drawing Kaplan-Meier plots starting from 0 instead of 1, similar like fun = 'event' in the standard plotting function used on a survfit object. I apologize in advance for having missed any obvious informational sources but I really didn't find anything in the

The answer to this may be obvious, but I was wondering in the rms package and the survfit(), how you can plot the censored time points as ticks. Take for example, library(survival) library(rms) foo <- data.frame(Time=c(1,2,3,4,5,6,10), Status=c(1,1,0,0,1,1,1)) answer <- survfit(Surv(foo$Time, foo$Status==1) ~1) # this shows the censored time points as ticks at Time = 3 and 4 plot(answer)

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

Hello, I apologize for the post as I am certainly overlooking a simple solution to my difficulties with getting a legend to print on a survplot from the rms package. I am plotting the following: survplot(survest(fita), n.risk=T, conf='none', cex.n.risk=.85, dots=T, col='gray10', lty=2) survplot(survest(fit), n.risk=F, conf='none', add=T) survplot(survest(fitb), n.risk=F,

Hi, in Design package, a plot of survival probability vs. a covariate can be generated by survplot() on a cph object using the folliowing code: n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age" sex <- factor(sample(c('male','female'), n, TRUE)) cens <- 15*runif(n) h <- .02*exp(.04*(age-50)+.8*(sex=='Female')) dt <-

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

I am using the package Design for survival analysis. I want to plot a simple Kaplan-Meier fit of survival vs. age, with age grouped as quantiles. I can do this: survplot(survfit(Surv(time,status) ~ cut(age,3), data=veteran) but I would like to do something like this: survplot(survfit(Surv(time,status) ~ quantile(age,3), data=veteran) #will not work ideally I would like to superimpose

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

Hi, when I generated a survfit() object, I can get number of patients at risk at various time points by using summary(): fit<-survfit(Surv(time,status)~class,data=mtdata) summary(fit) class=1 time n.risk n.event survival std.err lower 95% CI upper 95% CI 9.9 78 1 0.987 0.0127 0.963 1 41.5 77 1 0.974 0.0179 0.940 1 54.0 76

I've obtained a survival plot from the following code: s = Surv(outcome.[,1], outcome.[,2]) survplot= (survfit(s ~ person.list[,1])) plot(survplot, mark.time = FALSE) person.list is just a list of 15 people. When I plot this, the lines on my plot all end at different time points. Is there a way to extend all the lines to make them end at a certain time point? (i.e outcome.[,1]

Hi, I have a Cox PH model that's large for my server, 120K rows, ~300 factors with 3 levels each, so about 1000 columns. The 300 factors all pass a preliminary test of association with the outcome. Solving this with cph from Design takes about 3 hours. I have created the fit with x=T, y=T to save the model data. I want to validate the PH assumption by calling survplot(fit, gender=NA,

R-ers! Referees demand that the line in the KM-curve should be changed to dotted at the point where standarerror is <= 10 %. I don't think it's a good habit but I urgently need to implement such a thing in R with survfit, survplot or another program. They also want numbers at risk below the curve Some help, please.... Fredrik ######################## Fredrik Lundgren

Here is an example of how to do it. > library(survival) > vfit <- coxph(Surv(time, status) ~ celltype + trt, data=veteran) > userinput <- data.frame(celltype="smallcell", trt = 1) > usercurve <- survfit(vfit, newdata=userinput) #the entire predicted survival curve > user2 <- summary(usercurve, time= 2*365.25) # 2 year time point > user2$surv [1]

Dear R users, I know one way to see the code for a hidden function, say function_x, is using default.function_x (e.g. summary.default). But how can I see the code for imported packages that have no namespace (in this case Design)? Many Thanks Eleni