similar to: That 'make check-all' problem with the survival package

------------------------------ On Sat, May 16, 2015 8:04 AM BST Uwe Ligges wrote: >Not sure why this goes to R-devel. You just could have asked the >maintainer. Terry Therneau is aware of it and promised he will fix it. > The quickest fix is to add cmprsk to the recommended list , and that's is an R-devel issue. >On 16.05.2015 07:22, Hin-Tak Leung wrote: >> 'make

'make check-all' for current R has been showing this error in the middle for a few months now - any thought on fixing this? I think cmprsk should be either included in the recommended bundle, or the survival vignette to not depend on it. Having 'make check-all' showing glaring ERROR's for a few months seems to defeat the purpose of doing any checking at all via 'make

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that

Hello again, I m trying to use timereg package as you suggested (R2.7.1 on XP Pro). here is my script based on the example from timereg for a fine & gray model in which relt = time to event, rels = status 0/1/2 2=competing, 1=event of interest, 0=censored random = covariate I want to test library(timereg) rel<-read.csv("relapse2.csv", header = TRUE, sep = ",",

Hi all I do not understand why I am getting the following error message. Can anybody help me with this? Thanks in advance. install.packages("cmprsk") library(cmprsk) result1 <-crr(ftime, fstatus, cov1, failcode=1, cencode=0 ) one.pout1 = predict(result1,cov1,X=cbind(1,one.z1,one.z2)) predict.crr(result1,cov1,X=cbind(1,one.z1,one.z2)) Error: could not find function

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?).

Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10)

I am trying to use the crr function in the cmprsk package to analyze a large patient dataset (45000 +), The model has 100 + covariates and 5 competing risks. I am finding that R seems to get bogged down and even if I let it run for several hours I don't get anything back. Am I expecting too much, or are there ways to speed up the process? Any help is appreciated. Best, Spencer

Dear R users, I used crr function in R package 'cmprsk' to fit a competing risks model. There were no any error or warning messages during running the function, but the output was obvious not correct. I saved the model fit as an object called f.crr. I extracted bfitj from the object by doing f.crr$bfitj, and found values of bfitj were extremely large (around 1e+138). I tried all

Dear all, I was wondering if anyone can help? I am an R user but recently I have resorted to SAS to calculate the probability of the event (and the associated confidence interval) for the Cox model with combinations of risk factors. For example, suppose I have a Cox model with two binary variables, one for gender and one for treatment, I wish to calculate the probability of survival for the

Hi, I posted this question on stats.stackexchange.com 3 days ago but the answer didn't really address my question concerning the speed in competing risk regression. I hope you don't mind me asking it in this forum: I?m doing a registry based study with almost 200 000 observations and I want to perform a competing risk analysis. My problem is that the crr() in the cmprsk package is

hi, i've succesfully built R 1.6.0 on a ds10 with a " stock" redhat 6.2 all tests are ok i've installed the following packages survival and cmprsk succesfully but when i use the crr function (cmprsk package) i got a segmentation fault my question is : any one build a R version with ev6 processor (with or with out compaq fortran and c) thanks -- Get your free email from

Dear all, I am trying to use the crr function in the cmprsk package version 2.2 to analyse 198 observations.I have receive the error in solve.default. Can anyone give me some insights into where the problem is? Thanks here is my script : cov=cbind(x1,x2) z<-crr(ftime,fstatus,cov)) and data file: x1 x2 fstatus ftime 0 .02 1 263 0 .03 1 113 0 .03 1 523

Hi, I need to fit model using crr, however my covariate is categorical with 3 levels. I use crr(time,status,agesplit,failcode=1,cencode=0) where agesplit is defined as <20,21-29,>30 years, so it takes 0, 1 or 2 for each patient. I hoped to get estimated coefficients for the levels 1 and 2 w.r.t level 0 as in coxph. But, I didn't. Could someone please help me to use crr in this

Dear R users I would like to calculate the Cumulative incidence for an event adjusting for competing risks and adjusting for covariates. One way to do this in R is to use the cmprsk package, function crr. This uses the Fine & Gray regression model. However, a simpler and more classical approach would be to implement the Kalbfleisch & Prentice method (1980, p 169), where one fits cause

Hi all, I am aware that crr lacks the "friendly" command structure of functions such as cph. All is clear to me about including covariates until I want to include a stratification term in the competing risk framework (no nice strat command). I am still a bit of a novice in R - I am looking for an example to help me with this, but can't seem to find one. Any advice appreciated (no

Dear R-help, I'm very sorry to ask 2 questions in a week. I am using the package 'crr' and it does exactly what I need it to when I use the dataset a. However, when I use dataset b I get the following error message: Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number =

Dear R-users Has anybody implemented a function/package that will compute an individual's risk of an event in the presence of competing risks, adjusted for the individual's covariates? The only thing that seems to come close is the cuminc function from cmprsk package, but I would like to adjust for more than one covariate (it allows you to stratify by a single grouping vector). Any

hello,everyone: I am now confused in multistate survival , when I want to poccess a multistate survival analysis, I turn to R and the package 'mstate' and packege 'cmprsk'. When I come to publishing the article. Follow requirement of the magzine, the statistic is carry out in LTA package (which was said to be a authority in intauterine from WHO) . There we found difference

Dear Prof Gray and everyone, As our package developers discussed about incompatibility between Design and survival packages, I faced another problem with cmprsk- a survival dependent packacge. The problem is exactly similar to what happened to the Design package that when I just started running cuminc function, R was suddenly closed. These incidents suggest that maybe many other survival