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

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

I am trying to fit a lognormal distribution on interval-censored data. Some of my intervals have a lower bound of zero. Unfortunately, it seems like survreg() cannot deal with lower bounds of zero, despite the fact that plnorm(0)==0 and pnorm(-Inf)==0 are well defined. Below is a short example to reproduce the problem. Does anyone know why survreg() must behave that way? Is there an alternate

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

Hello, I'm working with some left censored survival data using accelerated failure time models. I am interested in fitting different distributions to the data but seem to be getting the same results from the model fit using survreg regardless of the assumed distribution. These two codes seem to provide the same results: aft.gaussian <-

Full_Name: Tim Cohn Version: 1.6.1 OS: Macintosh OS X Submission from: (NULL) (130.11.34.250) The Mac version of survreg does not handle left-censored data correctly (at least the results are not what I get doing it other ways, and they are not the same as I get running R 1.6.1 in Windows 98se; the Windows 98 results are correct). On the windows version of R 1.6.1. >

Dear R-user: I am using survreg(Surv()) for fitting a Tobit model of left-censored longitudinal data. For logarithmic transformation of y data, I am trying use survreg.distributions in the following way: tfit=survreg(Surv(y, y>=-5, type="left")~x + cluster(id), dist="gaussian", data=y.data, scale=0, weights=w) my.gaussian<-survreg.distributions$gaussian

What objective function is maximized by survreg with the default Weibull model? I'm getting finite parameters in a case that has the likelihood maximzed at Infinite, so it can't be a simple maximum likelihood. Consider the following: ############################# > set.seed(3) > Stress <- rep(1:3, each=3) > ch.life <- exp(9-3*Stress) > simLife <- rexp(9,

Dear R-list I have been trying to make survreg fit a normal regression model with left truncated data, but unfortunately I am not able to figure out how to do it. The following survreg-call seems to work just fine when the observations are right censored: library(survival) n<-100000 #censored observations x<-rnorm(n) y<-rnorm(n,mean=x) d<-data.frame(x,y) d$ym<-pmin(y,0.5)

Hello, I have some measurements that I am trying to fit a model to. I also have uncertainties for these measurements. Some of the measurements are not well detected, so I'd like to use a limit instead of the actual measurement. (I am always dealing with upper limits, i.e. left censored data.) I have successfully run survreg using the combination of well detected measurements and limits,

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 __________________________________

Hello! I have a matrix with data and a column indicating whether it is censored or not. Is there a way to apply weibull and exponential maximum likelihood estimation directly on the censored data, like in the paper: Backtesting Value-at-Risk: A Duration-Based Approach, P Chrisoffersen and D Pelletier (October 2003) page 8? The problem is that if I type out the code as below the likelihood

Dear r-help subscribers, A couple of weeks ago I sent the following message to the r-help mail list. It hasn't generated any response, and I could really use some help on this. Anyone able to help? Thanks again, Roger Dungan >> I am working on some survival analysis of some interval censored failure time data in R. I have done similar analysis before using PROC LIFEREG in SAS. In

Dear All, I would like to fit some parametric survival models using left truncated, right censored data in R. However I am having problems finding a function to fit parametric survival models which can handle left truncated data. I have tested both the survreg function in package survival: fit1 <- survreg(Surv(start, stop, status) ~ X + Y + Z, data=data1) and the psm function in package

Hi, what is the best way to estimate a tobit(truncated) regression model in R ? Is there already a packet available ? Gruss Ralph Leonhardt -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body",

Does anyone know if the survreg function in the survival package can fit numerical covariates ? When I fit a survival model of the form survreg( Surv(time,censored) ~ x ) then x is always treated as a factor even if it is numeric (and even if I try to force it to be numeric using as.numeric(x). Thus, in the particular example I am analysing, a simple numerical covariate becomes a factor

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Hello, I'm trying to calculate the Maximum likelihood estimators for a dataset which contains censored data. I started by using the function "nlm", but isn't there a separate method for doing this for e.g. the "weibull" and the "log-normal" distribution? Thanks, Olivia [[alternative HTML version deleted]]

Hi, I'm trying to figure out if there are any packages allowing one to fit mixed models (or non-linear mixed models) to data that includes censoring. I've done some searching already on CRAN and through the mailing list archives, but haven't discovered anything. Since I may well have done a poor job searching I thought I'd ask here prior to giving up. I understand that

Hi just wondering if there is a package that can get the maximum likelihood or method of moments estimator for distributions with censored data? The distributions I'm interested in are: Exponential, pareto, beta, gamma and lognormal. -- View this message in context: http://www.nabble.com/MLE-for-censored-distributions-in-R-tp15022863p15022863.html Sent from the R help mailing list archive at

dear all, I would like to know whether survdiff and survReg function in the survival package work for left-truncated and right-censored data. If not, what other functions can i use to make comparison between two survival curves with LTRC data. thanks for any help given sing yee