similar to: error fitting coxph model

Hi, I have a character vector that contains the names of several objects that I would like to pass to a function (specifically, the ridge function in the survival package, but cbind is a similar example). I've been struggling with how to do this so that the object values get interpreted by the function, rather than the object names. For example, x1 <- 1:4 x2 <- 2:5 x3 <-

I am trying to fit a large Cox model with many predictors. Because there are many predictors, I would like to use the ridge() function to get penalized ml estimates for all coefficients. The problems are that: 1. When I include a factor (like race) in the ridge() function, dummy variables are not created. The resulting model has a single coefficient for the race variable, and I have

I am fitting a coxph model on a large dataset (approx 100,000 patients), and then trying to estimate the survival curves for several new patients based on the coxph object using survfit. When I run coxph I get the coxph object back fairly quickly however when I try to run survfit it does not come back. I am wondering if their is a more efficient way to get predicted survival curves from a coxph

Hi, I'm trying to make a boxplot with the data at the end of the message, and when I try to execute the command >boxplot(Diatoms) (or for any other field instead of "Diatoms") I get the following error message: Error in oldClass(stats) <- cl : adding class "factor" to an invalid object Any advice would be much appreciated. Thanks a lot, Miriam Date

I have what I *think* should be a simple problem in R, and hope someone might be able to help me. I'm working with cancer survival data, and would like to calculate adjusted survival figures based on the age of the patient and the tumour classification. A friendly statistician told me I should use Cox proportional hazards to do this, and I've made some progress with using the

Hello, I was hoping that someone could answer a few questions for me (the background is given below): 1) Can the coxph accept an interaction between a covariate and a frailty term 2) If so, is it possible to a) test the model in which the covariate and the frailty appear as main terms using the penalized likelihood (for gaussian/t frailties) b)augment model 1) by stratifying on the variable that

I'm trying to estimate a cox proportional hazards regression for repeated events (in gap time) with time varying covariates. The dataset consists of just around 6000 observations (lines) (110 events). The (stylized) data look as follows: unit dur0 dur1 eventn event ongoing x 1 0 1 0 0 0 32.23 1 1 2 0 1 1 35.34 1

Hi Dear R-users I have a database with 18000 observations and 20 variables. I am running cox regression on five variables and trying to use survfit to plot the survival based on a specific variable without success. Lets say I have the following coxph: >library(survival) >fit <- coxph(Surv(futime, fustat) ~ age + rx, data = ovarian) >fit what I am trying to do is plot a survival

I have three users whose smbd processes will suddenly go rogue and start sucking up CPU time. It isn't guaranteed that every time they log in, that their process goes rogue, but it happens a lot. At that time I see that those smbd processes have an effective UID of the person's username, and a real UID of root. Everyone else's is always EUID root and UID root. I haven't a clue

An old problem has started to reappear with our Solaris (2.8) Samba (3.0.23b). This was after an upgrade from 3.0.22 in which the problem was worse. Occasionally an smbd process will go "rogue" and start consuming CPU cycles until killed. I did a truss of the process and got back this tight loop. fcntl(27, F_SETLKW64, 0xFFBEEF98) = 0 fcntl(27, F_SETLKW64, 0xFFBEF010)

This is a follow-on to my previous message about some 3.0.23b Solaris smbd processes going rogue and eating CPU time. One characteristic of the processes are that the effective UID of the process is the user's UID, rather than root's which is what a see for the well behaved processes. Any ideas how that could be happening so that I can get closer to a solution? Thanks. -- Jonathan M.

I have been looking at a draft of Doug Bate's book on mixed effects models (http://lme4.r-forge.r-project.org/book), and in chapter one (section "1.4.2 Matrices and Vectors in the Fitted Model Object", to be exact) he refers to the function env() for checking the environment of a fitted lmer object. This function does not appear under help, and I have not been able to find

Hi, I would like to fit some experimental points by a exponential function. I ignore the parameters of this exponential and what I would like is to ask R to calculate the best fitting curve an the associated parameters (as the linear model function (lm) does for linear models). Is it possible ? Do anyone have an idea about how to do that ? Thanks by advance Jessica Gervais [[alternative

Dear all, I have a data set<http://yaap.it/paste/c11b9fdcfd68d02b#gIVtLrrme3MaiQd9hHy1zcTjRq7VsVQ8eAZ2fol1lUc=>with 6 clusters, each containing 48 (possibly censored, in which case "event = 0") survival times. The "x" column contains a binary explanatory variable. I try to describe that data with a gamma frailty model as follows: library(survival) mod <-

Dear Thomas & Dear List, the fitting function `coxph.fit' called by `coxph' may fail to estimate the regression coefficients when some values of the design matrix are very large. For example library(survival) ### load example data load(url("http://www.imbe.med.uni-erlangen.de/~hothorn/coxph_fit.Rda")) method <- "efron" ### copied from `coxph.fit' coxfit

It seems to me that R returns the unpenalized log-likelihood for the ratio likelihood test when ridge regression Cox proportional model is implemented. Is this as expected? In the example below, if I am not mistaken, fit$loglik[2] is unpenalized log-likelihood for the final estimates of coefficients. I would expect to get the penalized log-likelihood. I would like to check if this is as expected.

Dear all, I have a question about time-dependent covariates in a coxph model. Specifically I am wondering whether it is possible to give more recent events a higher weight when constructing time-dependent covariates. Assume I have a sample of cancer patients and I would like to predict whether the number of treatments a patient received has an impact on survival time. For each patient in my

Dear List, In documents (Therneau, 2003 : On mixed-effect cox models, ...), as far as I came to know, coxme penalize the partial likelihood (Ripatti, Palmgren, 2000) where as frailtyPenal (in frailtypack package) uses the penalized the full likelihood approach (Rondeau et al, 2003). How, then, coxme and coxph(...frailty(id, dist='gauss')) differs? Just the coding algorithm, or in

Dear all, I am confused with the output of survfit.coxph. Someone said that the survival given by summary(survfit.coxph) is the baseline survival S_0, but some said that is the survival S=S_0^exp{beta*x}. Which one is correct? By the way, if I use "newdata=" in the survfit, does that mean the survival is estimated by the value of covariates in the new data frame? Thank you very much!

Hi there, I have a question on how to extract the linear term in the penalized spline in coxph. Here is a sample code: n=100 set.seed(1) x=runif(100) f1 = cos(2*pi*x) hazard = exp(f1) T = 0 for (i in 1:100) { T[i] = rexp(1,hazard[i]) } C = runif(n)*4 cen = T<=C y = T*(cen) + C*(1-cen) data.tr=cbind(y,cen,x) fit=coxph(Surv(data.tr[,1],