search for: interval2

Does Surv in psm handle interval2 data? The argument list seems to indicate it does but I get an error. Thanks, Chris # code library('survival') left <- c(1, 3, 5, NA) right <-c(2, 3, NA, 4) Surv(left, right, type='interval2') survreg(Surv(left, right, type='interval2') ~ 1) library('rms')...

Can anybody give me a neat example of interval censored data analysis codes in R? Given that suvreg(Surv(c(1,1,NA,3),c(2,NA,2,3),type="interval2")~1) works why does survreg(Surv(data[,1],data[,2],type="interval2")~1) not work where data is : T.1 T.2 Status 1 0.0000000 0.62873036 1 2 0.0000000 2.07039068 1 3 0.0000000 1.40297214 1 4 0.0000000 0.09112871 1 5 0.0...

...1 1 17 845 9.0 5.03 37 3.0 6 1 18 913 2.0 5.84 45 0.1 1 1 19 577 4.0 4.10 30 90.0 NA 0 20 20 0.5 7.44 8 24.0 27 1 library(rpart) library(survival) fit<-rpart(Surv(N,Y,type="interval2")~Salt+pH+Temp, data=myD) #--------------------- Professor Ripley helpfully pointed out that the documentation does not say that interval censoring is supported, and indeed this seems only to happen with interval censored data. ?rpart indicates that the dependent variable may be a survival...

I tried your code, but it's giving the following error.. Error in match.fun(FUN) : argument "FUN" is missing, with no default

...ime1, time2) pairs, each of which describes the interval of time over which the tag was lost. So an animal first captured at time 10 sans tag would be (0,10); with tag at 5 and without at 20 would be (5,20), and last seen with tag at 30 would be (30, NA). Then survit(Surv(time1, time2, type='interval2') ~ 1, data=yourdata) will give a curve that accounts for interval censoring. As a prior poster suggested, if the times are very sparse then you may be better off assuming a smooth curve. Use the survreg function with the same equation as above; see help("predict.survreg") for...

Hi, I am wondering how to specify interval-censored data in coxph? The example in the help page summary(coxph(Surv(start, stop, event) ~ x, data = test2)) is for counting process data, is the counting process data the same as interval-censored data? Thanks __________________________________

...to have zero (0) instead of NA on my data. Is there a command i can issue to replace the NA with zero (0) even if it is after generating the data?? Thank you library(survival) p1<-0.8;b<-1.5;rr<-1000 for(i in 1:rr){ r<-runif(45,min=0,max=1) t<-rweibull(45,p1,b) w=Surv(r,t,type="interval2") x[1:45]<-(w[,1]) u<-x[1:45] y[1:45]<-(w[,2]) v<-y[1:45] } w u v Chris G Researcher Institute for Mathematical Research UPM

...red. In the right censored data set, there is an effect of one factor that causes very different shapes in survival curves (and non-proportional hazards) and I'd really like to say that the same factor has a similar effect in the other data set. However, as the data should really go in an "interval2" Surv object, I can't use cox.zph or a plot of that to say anything statistical about the curve shapes or hazards. I can of course just plot the survival curves and say that they look like they have different shapes, but I was hoping you lot would have a better idea. Is there a manual way...

Can survreg() handle interval-censored data like the documentation says? I ask because the command: survreg(Surv(start, stop, event) ~ 1, data = heart) fails with the error message Invalid survival type yet the documentation for Surv() states: "Presently, the only methods allowing interval censored data are the parametric models computed by 'survreg'"

...idations that used Dxy * plot.summary.rms now produces cleaner output with fewer confidence levels * Several bug corrections --------------------------------------------------------------------- Changes in version 4.0-0 (2013-07-10) * Cleaned up label logic in Surv, made it work with interval2 (thanks:Chris Andrews) * Fixed bug in val.prob - wrong denominator for Brier score if obs removed for logistic calibration * Fixed inconsistency in predictrms where predict() for Cox models used a design matrix that was centered on medians and modes rather than means (thanks: David van...

I have some data measured with a coarsely-quantized clock. Let's say the real data are q<- sort(rexp(100,.5)) The quantized form is floor(q), so a simple quantile plot of one against the other can be calculated using: plot(q,type="l"); points(floor(q),col="red") which of course shows the characteristic stair-step. I would like to smooth the quantized

Hello, I have interval censored data, censored between (0, 100). I used the tobit function in the AER package which in turn backs on survreg. Actually I'm struggling with the distribution. Data is asymmetrically distributed, so first choice would be a Weibull distribution. Unfortunately the Weibull doesn't allow for zero values in time data, as it requires x > 0. So I tried the

Hi, all: I want to check small files' property(such as date, path, and so on) frequently. The files are stored in netwrok driver and their sizes vary from 2KB to 5KB. I found that Windows 2K outperform Linux/Samba very much after I campared the bench results. I am very confused about it and who can explain it? The computers' configurations are as follows: 1. PC Client It

Dear all, ?The code below is used to generate interval censored data but unfortunately there is an error with the ifelse which i am not able to rectify. ?Can somebody help correct it for me. Thank you t<-rexp(20,0.2)? v<-c(0,m,999)? y<-function(t,v){ ? z<-numeric(length(t (( ? ? s<-numeric(length(t (( ? ? ? for(i in 1:length(t)){ ? ? ? ? for(j in 1:length(v-1))? ? ? ? ? { ifelse

...gt; > Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes > > On a Leopard Mac with the Urbanek compiled 64 bit R, one sees this: > > > library(rpart) > > library(survival) > Loading required package: splines > > fit<-rpart(Surv(N,Y,type="interval2")~Salt+pH+Temp, data=myD) > > *** caught segfault *** > address 0x0, cause 'memory not mapped' > > Traceback: > 1: .C(C_rpartexp2, as.integer(length(dtimes)), > as.double(dtimes), as.double(.Machine$double.eps), keep = > integer(length(dtimes))) > 2: (...