similar to: Escobar&Meeker example survreg

Dear R-experts: I am using survreg() to estimate the parameters of a Weibull density having right-censored observations. Some observations are weighted. To do that I regress the weighed observations against a column of ones. When I enter the data as 37 weighted observations, the parameter estimates are exactly the same as when I enter the data as the corresponding 70 unweighted observations.

Dear list, I would like to simulate individual survival times from a model that has been fitted using the survreg procedure (library survival). Output shown below. My plan is to extract the shape and scale arguments for use with rweibull() since my error terms are assumed to be Weibull, but it does not make any sense. The mean survival time is easy to predict, but I would like to simulate

Hi all, I would like to ask whether anyone knows if the censorReg in S-plus regress the mean survival model or the hazard and if it uses the maximum likelihood in order to estimate the values? In addition, is there anyone knowing why censorReg does not exist in R and what's the difference between censorReg and survreg (besides the truncation)? Cheers,Skevi [[alternative HTML version

You asked about survival curves with age scale versus follow-up scale. > fit1 <- coxph(Surv(time/365.25, status) ~ t5 + id + age, data=stanford2) > surv1<- survfit(fit1) > surv1 n events median 0.95LCL 0.95UCL 157.000 102.000 1.999 0.898 3.608 > summary(surv1, times=3) time n.risk n.event survival std.err lower 95% CI upper 95% CI 3 46 85 0.451

I am not sure if this is the right place for that kind of questions, but I wondered that the recommended package survival did not pass R's check procedure without warnings: 1) unbalanced braces: * Rd files with unbalanced braces: * man/Surv.Rd * man/cluster.Rd * man/cox.zph.Rd * man/coxph.Rd * man/coxph.detail.Rd * man/date.ddmmmyy.Rd * man/lines.survfit.Rd *

Dear R users, My question is more methodology related rather than specific to R usage. Using time on study as time in a cox model, eg: library(Design) stanf.cph1=cph(Surv(time, status) ~ t5+id+age, data=stanford2, surv=T) #In this case the 1000-day survival probability would be: stanf.surv1=survest(stanf.cph1, times=1000) #Age in this case is a covariate. #I now want to compare the above

Hi all, Is there an equivalent to predict(...,type="linear") of a Proportional hazard model for a Cox model instead? For example, the Figure 13.12 in MASS (p384) is produced by: (aids.ps <- survreg(Surv(survtime + 0.9, status) ~ state + T.categ + pspline(age, df=6), data = Aidsp)) zz <- predict(aids.ps, data.frame(state = factor(rep("NSW", 83), levels =

Has anyone done this? Or any suggestions? > Samba team, > Before I send this to the samba list... > > I am currently trying to setup a 3rd samba server with a tried > and true smb.conf file. The only difference is an upgrade from samba > 1.9.18p7 to samba 1.9.18p8. The only "Non-standard" feature, is that > we are using an NT server to authenticate users, and

I am a student (and very to new to R) working on a senior design project that is attempting to determine the demand distributions for single copy newspaper draws at individual sales outlet locations. Our sales data is right-censored, because sell-outs constitute a majority of the data, and we are also testing the relevance of including covariates (weather, seasonality, economic condition, etc.).

Greetings, I'm seeing am odd printing problem on a Windows 2000 machine - the machine is NOT in the Samba controlled domain, but in a different "real" NT 4.0 controlled domain. The user on this machine is able to mount "shares" from my Samba servers, but since moving to 2.2.3a his is denied access to printers. Strange is he can access files on the same server that

Dear R-Helpers: I have tried everything I can think of and hope not to appear too foolish when my error is pointed out to me. I have some real data (18 points) that look linear on a log-log plot so I used them for a comparison of lm() and survreg. There are no suspensions. survreg.df <- data.frame(Cycles=c(2009000, 577000, 145000, 376000, 37000, 979000, 17420000, 71065000, 46397000,

Hey all, I am trying to use the survreg function in R to estimate the mean and standard deviation to come up with the MLE of alpha and lambda for the weibull distribution. I am doing the following: times<-c(10,13,18,19,23,30,36,38,54,56,59,75,93,97,104,107,107,107) censor<-c(1,0,0,1,0,1,1,0,0,0,1,1,1,1,0,1,0,0) survreg(Surv(times,censor),dist='weibull') and I get the following

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

Hi all, Sorry if this has been answered already, but I couldn't find it in the archives or general internet. Is it possible to implement the gompertz distribution as survreg.distribution to use with survreg of the survival library? I haven't found anything and recent attempts from my side weren't succefull so far. I know that other packages like 'eha' and

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

Full_Name: Dave Ramsey Version: 1.8.0 OS: win2000 Submission from: (NULL) (202.27.240.6) there is a bug in extractAIC.survreg in library MASS. A survreg model object has no component called "residuals". Hence n <- length(fit$residuals) returns 0 resulting in errors workaround: replace n <- length(fit$residuals) with n <- length(residuals(fit)) ### sorry: error

Hi,Just a quick one, does anyone know the command for accessing the standard errors from a survreg object? I can access the coefficients by model$coefficients, but I cant seem to find a command to access the errors. Any help would be greatly appreciated.Regards,Andre [[alternative HTML version deleted]]

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 <-

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?m trying to do some analysis using survreg function. I need to implement there my own distribution with density: lambda*exp(-lambda*y), where y = a1/(1+exp(-a2*x)). a1, a2 are unknown parameters and x >0. I need to get estimates of a1 and a2 (and lambda of course) I?m really not good at programming. Is there any way how to implement this distribution to survreg without