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  Hi there,   I am beginner in R and I have some basic question. Suppose I run a common procedure such as a t test or cox model like below:   out<-coxph( Surv(tstart,tstop, death1) ~ x1+x1:log(tstop+1) , test1,method=c("breslow"))    Which yields the following result:   Call: coxph(formula = Surv(tstart, tstop, death1) ~ x1 + x1:log(tstop +     1), data = test1, method =

Hi there, I have a rather large data set and perform the following cox model: test1 <- list(tstart,tstop,death1,chemo1,radio1,horm1) out1<-coxph( Surv(tstart,tstop, death1) ~ chemo1+chemo1:log(tstop+1)+horm1+horm1:log(tstop+1)+age1+grade1+grade1:log(tstop+1)+positive1+positive1:log(tstop+1)+size1+size1:log(tstop+1), test1) out1 Up to here everything works fine (with each covariate

Hi everyone, I was trying to mode the following exercise using R. The question: Set up a one pool model using numericintegration. The model will run from time 1 to time 30 using a time step of 1.The pool (A) will be fed by flux "inA" at a rate of 5 units per hour anddrained by flux "outA" at a rate of 20% per hour. At time 0, A has 5units. At time 30, what is the pool size of

in wine/dlls/qcap/v4l.c: ReadThread() --> OutputPin_GetDeliveryBuffer((OutputPin *)capBox->pOut, &pSample, NULL, NULL, 0); --> IMediaSample_SetTime(*ppSample, tStart, tStop); in wine/dlls/quartz/memallocator.c: StdMediaSample2_GetTime(IMediaSample2 * iface, REFERENCE_TIME * pStart, REFERENCE_TIME * pEnd) --> if tstart/stop is NULL, leave pStart/pEnd not setted. in

Hello R users I have a question with Kaplan-Meier Curve with respect to my research. We have done a retrospective study on fillings in the tooth and their survival in relation to the many influencing factors. We had a long follow-up time (upto 8yrs for some variables). However, we decided to stop the analysis at the 6year follow up time, so that we can have uniform follow-up time for all the

tftp-hpa-0.35 and tftp-hpa-0.36 show wrong transmission time. >tftp -V tftp-hpa 0.36, with readline >tftp -m octet -v localhost -c get aaa /tmp/aaa Connected to localhost.localdomain (127.0.0.1), port 69 getting from localhost.localdomain:aaa to /tmp/aaa [octet] Received 33554432 bytes in -3.3 seconds [-81407477 bit/s] ^^^^ ^^^^^^^^^ By the way, why

Hi , I have a data set with recurrence time (up to four) of myocardial infarction (MI). Part of the file is showing below: Num1 Trt Sex Time T1 T2 T3 T4 1011 1 1 9 1211 0 1 59 3020 1 2 14 3 1245 0 1 18 12 16 3069 1 2 26 6 12 13 2051 0 1 53 3 15 46 51 The data consist of the following eight variables: Num1 , patient number Trt, treatment group (1=placebo and 2=drug) Sex,

Hello, I have a cohort with approx 1,200 patients at the ages of 30-65 that had their first myocardial infarction during 1992: · They were in a follow up until 2005. · About 400 of them died during this period of time (right censored) · Each one of them had up to 4 mi recurrent events. I am using the semi-parametric model in order to assess the relationship of

Hi, when i run the following code i get this massege: "The instruction at 0x######## reference memory at 0x#######, the memory cannot be "read". and then i have to close R. what is the problem and how can i solve it? thanks in advance Avi my code # frailtypack library(frailtypack) cgd.ag <- read.csv("C:/rfiles/RE/cgd.csv") cgd.nfm <-frailtyPenal(Surv(TStart,

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),

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

I haven't done any R memory debugging lately, but https://www.mail-archive.com/rcpp-devel at lists.r-forge.r-project.org/msg10289.html shows how I used to have gdb break where valgrind finds a problem so you could examine the details. Also, running your code after running gctorture(TRUE) can help track down memory problems. -Bill On Wed, Feb 7, 2024 at 12:03?PM Therneau, Terry M., Ph.D.

Hi, I wanted to send a small patch for survival documentation. So I forked, patched and tried to build locally using R CMD build, but this failed with errors about undefined references. Upon closer inspection, the undefined references relate to files that give a warning when compiled through install.packages("survival", type = "source") on Windows, for example:

Hi, I am currently analysising a counting process form of a cox model allowing for the inclusion of time dependent covariates. An example model I have fitted is modlqol<-coxph(Surv(Tstart,Tstop,cens.time)~tmt.first+risk +lqol+cluster(id),data=cat) summary(modlqol) My question is quick. I am looking at 1 event (death), and repeated measurements (the time dependent covariate 'lqol')

?I've hit a roadblock debugging a new update to the survival package.?? I do debugging in a developement envinment, i.e. I don't create and load a package but rather? source all the .R files and dyn.load an .so file, which makes things a bit easier. ? Running with R -d "valgrind --tool=memcheck --leak-check=full" one of my test files crashes in simple R code a dozen lines

Dear all, in order to estimate transition-specific probabilities in a multi-state model i applied the 'probtrans()' function from the 'mstate'-package. Now, i am at loss with the following message (see attached example): Warning message: In probtrans(msf.0, predt = 0) : Negative diagonal elements of (I+dA); the estimate may not be meaningful. I am not very familiar with matrix

Maybe trivial question, but I cannot find an answer: How to autostart Asterisk (daemon) on Slackware 10? I know that I should put something in /etc/rc.d, but what?

Dear all, This probably stems from my lack of understanding of the model, but I do not understand the variance of the random effect reported in coxme. Consider the following toy example: #------------------------------- BEGINNING OF CODE ------------------------------------------------ library(survival) library(coxme) #--- Generate toy data: d <- data.frame(id = c(1:100), #