similar to: Competing risks - calibration curve

Dear Geskus, I want to develop a prediction model. I followed your paper and analysed thro' weighted coxph approach. I can develop nomogram based on the final model also. But I do not know how to do internal validation of the model and subsequently obtain calibration plot. Is it possible to use Wolbers et al Epid 2009 approach 9 (R code for internal validation and calibration) . It is

Dear All, I would like to use R function 'selectFGR' of fine gray model in competing risks model. I used the 'Melanoma' data in 'riskRegression' package. Some of the variables are factor. I get solution for full model but not in variable selection model. Any advice how to use factor variable in 'selectFGR' function. The following R code is produced below

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),

Dear Raja, A Fine and Gray model can be fitted using the standard coxph function with weights that correct for right censoring and left truncation. Hence I guess any function that allows to perform stepwise regression with coxph should work. See e.g. my article in Biometrics https://doi.org/10.1111/j.1541-0420.2010.01420.x, or the vignette "Multi-state models and competing risks" in the

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

Hi , library(foreign) write.dta(data1, "data1.dta") should work. The file will be saved in the working directory. Use getwd() to know the working directory. Best wishes Amalraj Raja -----Original Message----- From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of rosario scandurra Sent: 22 March 2018 07:47 To: r-help at r-project.org Subject: [R] exporting data to stata

On Thu, Mar 22, 2018 at 4:52 AM, Raja, Dr. Edwin Amalraj <amalraj.raja at abdn.ac.uk> wrote: > Hi , > > library(foreign) > write.dta(data1, "data1.dta") > > should work. I don't think so: > library(foreign) > example(svydesign) > write.dta(dstrat, "~/Downloads/foo.dta") Error in write.dta(dstrat, "~/Downloads/foo.dta") : The

Hello, We previously had Win2008 R2 DCs which we have begun to replace with Win2012 servers (forest is still at 2008 level however). I was able to query DFS via rpcclient but now I find that I am unable to. I know that SMB3 was introduced in Win2012 but I believe that it should auto-negotiate earlier versions. I did however test from v4.1.9 as well, unfortunately that still fails. From Samba3

Hi, I am new to R and I want to export data into Stata. Could somebody help with that? Thanks a lot. This is the code I am using: > setwd("D:/datasets/Seg-bcn/ESBD") > data1 <- readRDS("r17045_ESDB_Habitatges_BDD_V_1_0.rds") > library(foreign) > write.dta(data="data1", file = "D:/datasets/data1.dta") Error in write.dta(data =

Hello, I am currently trying to show the abundance of two species of zooplankton within the North Sea as pie chart bubble plots. I followed Werner Wernersen's advice in R help (http://finzi.psych.upenn.edu/R/Rhelp02a/archive/48644.html) and used Paul Murrell's paper "Integrating Grid Graphics Output with Base Graphics Output" (in R News) to try and do this (using the gridBase

Hi there, I'm a PhD student investigating growth patterns in fish. I've been using the minpack.lm package to fit extended von Bertalanffy growth models that include explanatory covariates (temperature and density). I found the nls.lm comand a powerful tool to fit models with a lot of parameters. However, in order to select the best model over the possible candidates (without covariates,

Representative small sample of data: algorithmID <- factor(c(rep('alg1',4),rep('alg2',4),rep('alg3',4))) threshold <- factor(rep(c(.45,.50,.55,.60),times=3)) score <- c(30,32,31,30,10,12,13,14,22,21,20,24) d <- data.frame(algorithmID,threshold,score) AlgorithmID is the name of each algorithm; threshold is the value of a parameter used by the algorithm that

I am applying a GLS model and would like to look at diagnostic plots of influence. The function (plot(model)) that works for linear models does not seem to function for GLS models. Is there a reason for this? Or is different code required? Sorry if it's a very basic question, but thanks for your help! The University of Aberdeen is a charity registered in Scotland, No SC013683.

Hi I am using Ubuntu 8.04 64 bit, R as below, Matlab 7.6.0. I would like to transfer mat files back and forward between R and Matlab. Whilst I have used Matlab for years its been a long time since I have used R (hence question may be a bit simple) Running code A <- c(1:10) dim(A) <- c(2,5) library(R.matlab) writeMat('A.mat', A=A) Does not appear to generate any mat file

Hi, I realize the answer is very likely in the section Generic functions and methods (or Adding new generics), but I'm not clear what to do with the following. Running R CMD check, I get the following warnings for my generic functions. Does this mean I need the argument * checking generic/method consistency ... WARNING leps: function(x, ...) leps.default: function(x, pred, titl,

Dear R group: I am compiling a list of available algorithms/macros/software for performing the following types of competing risks analyses: (1) cumulative (crude) incidence regression (2) Gray's K-sample log-rank test for cmulative incidence (3) multi-state models (4) dependent competing risks modeling with (a) copulas, (b) Robin's Inverse probability of censoring weighted (IPCW)

Dear R users: Analysis of the impact of a time-dependent covariate (GVHD or use of steroid after bone marrow transplantation) on two competing endpoints (invasive fungal infection and death) is frequently encountered in the setting of BMT data. Coxph package can be used as the following: for the analysis of GVHD: > gvhd -> coxph(Surv(start,stop,status = =1) ~ GVHD, data=bmt.data)

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 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

All, I am working with Emergency Department (ED) Length of Stay Data. The ED visit can end in one of a variety of ways (Admit, discharge, transfer, etc...) Initially, I have modeled the time to event by fitting a survival model to the time the outcome of interest and treat all other outcomes as censoring. However I recently came across the cmprsk package in R which seems to be developed