similar to: [UNCLASSIFIED] predict.survreg() with frailty term and newdata

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

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

It can't be done with the current code. In a nutshell, you are trying to use a feature that I never got around to coding. It's been on my "to do" list, but may never make it to the top. Terry

Rolf turner wrote: >I have been trying, unsuccessfully, to use identify() to (simply) >return a list of the indices of points clicked on and overplot (with >say a solid dot) each clicked-on point so that I can see where I've >been. I.e. I don't want to see the indices printed on the screen; I just want the points I've already selected to be highlighted. > >I

Hi, You can exit out of the 'identify()' routine by either: right-click > 'stop' Or click on 'stop > stop locator' in the top-left of the plot window. The script should continue with line1, line2 etc. Cheers Joe Joe Crombie Information and Risk Sciences Bureau of Rural Science Canberra Australia p: +61 2 6272 5906 e: joe.crombie at brs.gov.au

Hi Tom, Try this: > G <- "Number: %s" > sprintf(G, toString(A)) [1] "Number: 3, 4, 5" Cheers Joe Joe Crombie Biosecurity and Information Sciences Bureau of Rural Science Canberra Australia p: +61 2 6272 5906 e: joe.crombie at brs.gov.au -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of

Hi Louis, You could try this: # find the index of the maximum value in each row of _data_, # disregarding the last column classified <- apply(data[,-(nclass+1)],1,which.max) ## or, if the maximum may be repeated: classified <- apply(data[,-(nclass+1)], 1, FUN = function(x) which(x == max(x))) # the variable _truth_ is just the last column of _data_ ? truth <- data[,nclass + 1] ?table

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: >

On Tue, 6 May 2003, Jerome Asselin wrote: > > 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

Hi Fang, An easy way of doing this is by: > matplot(one, pch = LETTERS[1:4], type = 'b') Cheers Joe Joe Crombie Information and Risk Sciences Bureau of Rural Science Canberra Australia p: +61 2 6272 5906 e: joe.crombie at brs.gov.au -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of fang liu Sent: Friday,

SEE ALSO ORIGINAL POSTING IN PR#2933 On May 6, 2003 03:58 pm, Thomas Lumley wrote: > > Looking at a wider context in the code > > pfun <- function(coef, theta, ndeath) { > if (theta == 0) > list(recenter = 0, penalty = 0, flag = TRUE) > else { > recenter <- log(mean(exp(coef))) > coef <- coef - recenter

> 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

Hi, I would like to fit parametric survival models to time-to-event data that are left truncated. I have checked the help page for survreg and looked in the R-help archive, and it appears that the R function survreg from the survival library (version 2.16) should allow me to take account of left truncation. However, when I try the command

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-list users, I am trying to do simulation of survival data to enable it to run under frailty option. Below is the function a that I am using. My questions are: 1. How do I modify it to get bigger (hopefully significant) value of Variance of random effect? 2. What changes do I have to make in the function to run it under correlated frailty model? (may be in kinship package) 3. Is there

I am using survreg from the survival package to run a left censored tobit model on “non-survival” data. I have to four questions that I hope someone can help me with: 1) Is there anything I should take into consideration when using frailty() to estimate random intercepts? 2) Is there anyway of extracting the estimated intercepts produced by survreg when using frailty()? 3) Can someone point

Hi everybody. I'm trying to fit a weibull survival model with a spline basis for the predictor, using the survival library. I've noticed that it doesn't seem to be possible to use the aic method to choose the degrees of freedom for the spline basis in a parametric regression (although it's fine with the cox model, or if the degrees of freedom are specified directly by the user),

Hi all, I am trying to run Weibull PH model in R. Assume in the data set I have x1 a continuous variable and x2 a categorical variable with two classes (0= sick and 1= healthy). I fit the model in the following way. Test=survreg(Surv(time,cens)~ x1+x2,dist="weibull") My questions are 1. Is it Weibull PH model or Weibull AFT model? Call: survreg(formula = Surv(time, delta) ~ x1

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

I am trying to model survival data with a Weibull distribution using survreg. Units are clustered two apiece, sometimes receiving the same treatment and sometimes opposing treatment.