similar to: Survreg(), Surv() and interval-censored data

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') Surv(left, right, type='interval2') # error args(Surv)

Hi Everyone, This example code results in R 'crashing'; that is the R application closes with no warnings or error messages. #----------------------- myD <- read.table(stdin(), header=TRUE, nrows=20) Broth Salt pH Temp N Y Growth 1 310 9.0 2.92 10 90.0 NA 0 2 615 6.0 7.82 30 1.0 2 1 3 217 2.0 7.34 10 7.0 8

Hi all! I have two survival data sets looking at similar effects in different settings. One data set is only right censored, but the other is interval censored. 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

Hello, ?When i generate data with the code below there appear NA as part of the generated data, i prefer 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)

Hi, I am trying to write a loop to recode my data from -999 to NA in R. What's the most efficient way to do this? Below is what I'm presently doing, which is inefficient. Thanks, Chris dat0 <- read.table("time1.dat") colnames(dat0) <- c("e1dq", "e1arcp", "e1dev", "s1prcp", "s1nrcp", "s1ints",

Better would be to use interval censored data. Create your data set so that you have (time1, 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,

The rms ("Regression Modeling Strategies") package has undergone a massive update. The entire list of updates is at the bottom of this note. CRAN has the update for linux and will soon have it for Windows and Mac - check http://cran.r-project.org/web/packages/rms/ for availability. This rms update relies on a major update of the Hmisc package. The most user-visible changes are:

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

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 __________________________________

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'"

Hi, I am a new user of R 2.8.1. I use Tinn-R for code editing. I use a windows 2003 system with 1 GB RAM. I am interested to generate dashboard and reports based on data from MS Access. These reports need to be posted on a weekly basis to the web. The reporting interface should provide facilities for "what if" scenarios. Is it possible to interface R analysis results to good open

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