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

------------------------------ On Sat, May 16, 2015 2:33 PM BST Marc Schwartz wrote: > >> On May 16, 2015, at 6:11 AM, Hin-Tak Leung <htl10 at users.sourceforge.net> wrote: >> >> >> >> ------------------------------ >> 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

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 ?).

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 =

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)

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 = ",",

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 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

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

Dear all, Happy New year! I have used the 'crr' function to fit the 'proportional subdistribution hazards' regression model described in Fine and Gray (1999). dat1 is a three column dataset where: - ccr is the time to event variable - Crcens is an indicator variable equal to 0 if the event was achieved, 1 if the event wasn't acheived due to death or 2 if the event wasn't

Dear R-help list members, I am trying to create my own axes when plotting a cumulative incidence curve using the plot.cuminc function in the CMPRSK library. The default x-axis places tick marks and labels at 0, 20, 40, 60, and 80 (my data has an upper limit of 96), whereas I want them at my own specified locations. Here is my example code: library(cmprsk) attach(MYDATA) MYCUMINC <-

Dear All, I have analysed time to event data for continuous variables by considering the multivariable fractional polynomial (MFP) model and comparing this to the untransformed and log transformed model to determine which transformation, if any, is best. This was possible as the Cox model was the underlying model. However, I am now at the situation where the assumption that the competing risks

Dear Thomas, I want to use evaluate effect of Age using restricted cubic form in the FGR function as Fgr.crr <- FGR(Hist(time, event) ~ rcs(Age_years), data=dat) It provides error. " Error in parse(text = termtext, keep.source = FALSE): .... 1: response ~ rcs(Age_years Do I need to change any of the R code? Regards Amalraj Raja The University of Aberdeen is a charity

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

Hola a todos, Me gustaria saber si en un modelo mixto se puede usar el tamaño de muestra como peso o es incorrecto hacerlo. Por ejemplo en el comando 'lmer' del paquete lme4 hay una opcion 'weights' (igual que en 'lm' de stats). Si tengo datos de una medida (por ejemplo el peso) de 10 especies de aves (rango, de 50 a 3000 datos segun la especie) ¿puedo usar esta n en

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