similar to: Meaning of pterms in survreg object?

I have the following questions about the variance of the random effects in the survreg() function in the survival package: 1) How can I extract the variance of the random effects after fitting a model? For example: set.seed(1007) x <- runif(100) m <- rnorm(10, mean = 1, sd =2) mu <- rep(m, rep(10,10)) test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution =

Dear R-users, I have noticed small discrepencies in the reported estimate of the variance of the frailty by the print method for survreg() and the 'theta' component included in the object fit: # Examples in R-2.6.2 for Windows library(survival) # version 2.34-1 (2008-03-31) # discrepancy fit1 <- survreg(Surv(time, status) ~ rx + frailty(litter), rats) fit1 fit1$history[[1]]$theta

Dear List, How do I extract the approximate Wald test for the frailty (in the following example 17.89 value)? What about the P-values, other Chisq, DF, se(coef) and se2? How can they be extracted? ######################################################> kfitm1 Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id, dist = "gauss"), data = kidney)

> I was wondering why survreg (in survival package) can not handle > three-way interactions. I have an AFT ..... You have given us no data to diagnose your problem. What do you mean by "cannot handle" -- does the package print a message "no 3 way interactions", gives wrong answers, your laptop catches on fire when you run it, ....? Also, make sure you read

Dear R-Helpers, I am hoping to perform survival analyses using the "ex-Gaussian" distribution. I understand that the ex-Gaussian is a convolution of exponential and Gaussian distributions for survival data. I checked the "survreg.distributions" help and saw that it is possible to mix pre-defined distributions. Am I correct to think that the following code makes the

Hi, I'm using survreg() from the survival package for parametric survival regression (modelling inter-arrival times of patients to a waiting list as exponentially distributed, with various regressors such as queue size and season). Does anyone know which algorithm survreg() uses for this? Thanks, Gad -- Gad Abraham Department of Mathematics and Statistics The University of Melbourne

What is the least surprising way of initializing a factor with predefined levels and with length 0? as.factor(c("eins", "zwei", "drei"))[FALSE] does the job but looks a bit weird. -- Johannes H?sing There is something fascinating about science. One gets such wholesale returns of conjecture mailto:johannes at

Hi all! Do you know if there is any R function/package that can be used to estimate "tobit" models with panel data (e.g. with random individual effects)? In economics, a "tobit" model is a model with a dependent variable that is left-censored at zero. Hence, it is a special case of a survival model and can be estimated using the "survival" package (see e.g.

I am failing to uncover Sweave chunks step by step using the LaTeX beamer class. The following minimal example: \documentclass{beamer} \usepackage{Sweave} \begin{document} \begin{frame}[fragile] In the year \uncover<2->{25}\uncover<3->{\Sexpr{5*5}} \uncover<4->{ <<echo=TRUE, print=TRUE>>= 5*5*101 @ } \end{frame} \end{document} leads to an error message when

I am about to give an introduction to R to some clinical data managers used to SAS. There is already a lot of material in printed form and on the web that paves the way. What I haven't found so far are text wrapping capabilities in setting tables in raw text as in SAS PROC REPORT. At the moment i would direct them at producing HTML output from R and pipe the result through lynx. Coming from

Whenever I try to load the Matrix package, I get the following error message: libRlapack.so: cannot open shared object file: No such file or directory A file with that name is indeed not on the hard disk. I am using the R version which comes with Ubuntu Hardy Heron LTS. Here is the output of R.Version(): R.Version() $platform [1] "i486-pc-linux-gnu" $arch [1] "i486" $os [1]

Hi, I was wondering if anybody might have a reference for me: My R code is growing and getting more and more confusing. Thus, I figure it's time to switch to object-oriented again. I have done oo programming in C++ and Java before but the first few tutorial on R oo were a bit confusing for me. Is there any brief tutorial on oo programming in R especially for people who have done oo in Java

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

I'm guessing there's a more efficient way to do the following using the index features of R. Appreciate any thoughts.... for (i in 1:nrow(dbs1)){ if(dbs1$Payor[i] %in% Payor.Group.Medicaid) dbs1$Payor.Group[i] = "Medicaid" if(dbs1$Payor[i] %in% Payor.Group.Medicare) dbs1$Payor.Group[i] = "Medicare" if(dbs1$Payor[i] %in% Payor.Group.Commercial)

Hi all, I can't come over a problem with formula. Suppose I have a coxmod model with the following formula: > somemod$formula Surv(lebzeit, tot == 1) ~ sex + (alter >= 65) + diff3k + zelltyp_k_c + q_nuc_3k + kar_k80_g80 + stadium and I want to drop the stadium explanatory variable from the model with > update(somemod, ". ~ . - stadium") I get the following messages:

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

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

I am confused on how the log-likelihood is calculated in a parametric survival problem with frailty. I see a contradiction in the frailty() help file vs. the source code of frailty.gamma(), frailty.gaussian() and frailty.t(). The function frailty.gaussian() appears to calculate the penalty as the negative log-density of independent Gaussian variables, as one would expect: >

The combination of survreg + gamma frailty = invalid model, i.e., the example that you quote. I did not realize that this had been added to the survreg help file until very recently. I will try to fix the oversight. Other, more detailed documentation states that Gaussian frailty + AIC is the only valid random effects choice for survreg. Details: frailty(x) with no optional