search for: tstop

  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 = c("breslow"))                    coef exp(coef) se(coef)     z    p x1     ...

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 having a length...

...nswer sheet provide the value of pool could be (23.8, 24.8, 25.0 or 24.7). Thevalue I got does not correspond to any of the choices given above. ?I don't know where I got it wrong.? Below is the Ro code I tried to estimate thepool size of A at time 30. #Step 1: Specify time? Tstart <- 1 Tstop <- 30 Tstep <- 1 #Step 2: Set variables corresponding to pools inA<-5 A <- inA t <- Tstart #Step 3: Set up rate parameters A<-5 kinA <- 5 koutA <- 0.2 #Step 4: Write arrays that track pool size overtime val <- array(1, dim=c(1, length(seq(Tstart,Tstop, by=Tst...

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 wine/dlls/quartz/videorenderer.c: VideoRenderer_Sample(LPVOID iface, IMediaSample * pSample) -->...

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

...^^^^ ^^^^^^^^^ By the way, why don't you use timersub() or something like it? --- tftp-hpa-0.36.org/tftp/tftp.c 2004-01-09 05:47:00.000000000 +0900 +++ tftp-hpa-0.36/tftp/tftp.c 2004-02-18 22:17:27.000000000 +0900 @@ -422,8 +422,8 @@ { double delta; - delta = (tstop.tv_sec+(tstop.tv_usec/100000.0)) - - (tstart.tv_sec+(tstart.tv_usec/100000.0)); + delta = (tstop.tv_sec+(tstop.tv_usec/1000000.0)) - + (tstart.tv_sec+(tstart.tv_usec/1000000.0)); if (verbose) { printf("%s %lu bytes in %.1f seconds", direction, amount, delta); printf(" [%....

...s: ID, patient?s identification (which is the sequence number of the subject) Visit, visit number (with value k for the kth potential MI recurrence) TStart, time of the (k?1)th recurrence for Visit=k, or the entry time 0 if VISIT=1, or the follow-up time if the (k?1)th recurrence does not occur TStop, time of the kth recurrence if Visit=k or follow-up time if the kth recurrence does not occur Status, event status of TStop (1=recurrence and 0=censored) For instance, patient # 3 with only one recurrence time at month 3 who was followed until month 14 will have values for Visit, TStart, TStop,...

...The exposure variable ("ESHKOL") is the socio-economic of the neighborhood while adjusting for additional variables such as age, sex, etc. In addition each patient has geo- statics -code for his neighborhood ("stat_unit"). I ran an Andersen Gill model: Ag<-coxph(Surv(TStart,TStop,Status)~ ESHKOL+VAR1+ VAR2+cluster(id),data=recmi) My questions are: · I would like to check the unique effect of the "stat_unit" variable. How can I write it in code? · In your opinion does the AG model is the best model for this type of data? Or should I use models th...

...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, TStop, Status)~cluster(Center)+subcluster(ID) Treatment,data=cgd.ag,Frailty=TRUE,n.knots=8,kappa1=50000, cross.validation=TRUE,recurrentAG=TRUE) -- View this message in context: http://r.789695.n4.nabble.com/memory-problem-tp2330510p2330510.html Sent fro...

...df$cause),0,df$cause) table(df$cause) df.w<-crprep("time","cause", data=df, trans=c(1,2), cens=0, id="nid", keep=c("x", "x.1")) with(df.w,table(failcode,status)) ddist<-datadist(df.w) options(datadist='ddist') mod<-cph(Surv(Tstart,Tstop,status==1)~rcs(x,3)+x.1,data=df.w, weight=weight.cens, subset=failcode==1, x=T, y=T, surv=T, time.inc = 2.5) mod2<-cph(Surv(Tstart,Tstop,status==1)~(x.1+rcs(x,3))^2,data=df.w, weight=weight.cens, subset=failcode==1, x=T, y=T, surv=T, time.inc = 2.5) mod2 ########################## To develop n...

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 reprise the original message to which you are responding. Terry Therneau On 10/09/2013 05:00 AM, r-help-request at r-project.org wrote: > Hello, > > I am encountering very similar problems with...

...dy allowed me to find a few other (mostly dumb) > errors that led > to an out of bounds access, e.g., the wrong endpoint variable in a for( ) > loop. What > would others advise as a next step? > > Here is the last part of the screen > > fit2 <- coxph(list(Surv(tstart, tstop, bstat) ~ 1, > + c(1:4):5 ~ age / common + shared), id= id, > istate=bili4, > + data=pbc2, ties='breslow', x=TRUE) > > surv2 <- survfit(fit2, newdata=list(age=50), p0=c(.4, .3, .2, .1, 0)) > > test2 <- mysurv(fit2, pbc2$bili4, p0...

...gw_32/bin/gcc -I"D:/R/R-35~1.0/include" -DNDEBUG -O3 -Wall -std=gnu99 -mtune=generic -c agfit4.c -o agfit4.o agfit4.c: In function 'agfit4': agfit4.c:334:20: warning: 'dtime' may be used uninitialized in this function [-Wmaybe-uninitialized] if (tstop[p] < dtime) break; /* no more to add */ When I build using R CMD build however, the file agfit4.c is never even built. That explains the undefined references, but I have no clue what the difference is between both methods. I suspected a newer version so I downloaded the source from CRAN manual...

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') are frequently taken on a subject, so I assume that measurements on the same subject will be correl...

...The valgrind approach had already allowed me to find a few other (mostly dumb) errors that led to an out of bounds access, e.g., the wrong endpoint variable in a for( ) loop.??? What would others advise as a next step? Here is the last part of the screen > fit2 <- coxph(list(Surv(tstart, tstop, bstat) ~ 1, +??????????????????? c(1:4):5 ~ age / common + shared), id= id, istate=bili4, +?????????????? data=pbc2, ties='breslow', x=TRUE) > surv2 <- survfit(fit2, newdata=list(age=50), p0=c(.4, .3, .2, .1, 0)) > test2 <- mysurv(fit2, pbc2$bili4, p0= 4:0/10, fit2, x0 =50) =...

...), status = c(NA, "s0", "s1", "s2"), data= S1, trans=tmat, keep=covs) # add 'transition-specific' covariates SLV <- expand.covs(SL, covs, append=TRUE, longnames=TRUE) head(SLV) names(SLV) # fit stratified Markov model cox1 <- coxph(Surv(Tstart, Tstop, status) ~ (v1.1 + v2.1) * v3.1 + (v1.2 + v2.2) * v3.2 + (v1.3 + v2.3) * v3.3 + strata(trans), SLV, method = "breslow") summary(cox1) # 'cloned' data for making predictions: specific covariate 'v3' fixed at 0 (& 1): SLV0 <- SLV[1:3,] SLV0$v3.1 <- 0 SLV0$v3.2...

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), #