similar to: what kind of residuals are the ones calculated in coxph?

Hi list, I was trying to use "predict.coxph" to calculate martingale residuals on a test data, however, as pointed out before http://tolstoy.newcastle.edu.au/R/e4/help/08/06/13508.html predict(mycox1, newdata, type="expected") is not implemented yet. Dieter suggested to use 'cph' and 'predict.Design', but from my reading so far, I'm not sure they can

Hello Gurus: If I have a dataframe with one of the variables called "age" for example, and I want to extract a random 10% of the observations from each "age" group of the entire data frame. Do I have to double loop to split the data and then loop again to assign random numbers? Or is there a better way to do this? Thanks! Karen

Dear R users, I tried to analysis the hazard function of some data by coxph function in survival package. The type of the data include "left-censored", "right-censored", "both right-censored and left-censored" (btw, does this has a technical term?), and "complete" ones. I noticed that event (one parameter in "Surv()") might be an indicator for the

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, My question concerns model selection, stepAIC(), add1(), and coxph(). In Venables and Ripley (3rd Ed) pp389-390 there is an example of using stepAIC() for the automated selection of a coxph model for VA lung cancer data. A statistics question: Can partial likelihoods be interpreted in the same manner as likelihoods with respect to information based criterion and likelihood ratio tests?

Hi Gurus: If I have a large dataset of the form of: > x <- data.frame(V1 = runif(10), V2 = sample(c('A','B','C'),10,T)) > x V1 V21 0.2691580 A2 0.8711267 B3 0.2674728 C4 0.3278876 A5 0.1809152 A6 0.2499651 C7 0.9155174 A8 0.8004974 B9 0.7885516 A10 0.9301630 A And I want a V3 that =V2 if V2=A, and =D if V2=B or C. In other words I

Hi, I am getting errors of the following kind. I can't seem to point the source of the error. I would greatly appreciate any advice. Many thanks and good day, -Melinda Error message : ---------------- "Ran out of iterations and did not converge in: fitter(X, Y, strats, offset, init, control, weights = weights,..." Details : --------- E is a vector of survival times (or censored

Is there an implementation of the Cox-Snell residuals / Nelson-Aalen plot for goodness of fit? Or otherwise is there an appropriate Goodness of Fit diagnostic? Thanks Murray -- Murray Pung Statistician, Datapharm Australia Pty Ltd 0404 273 283 [[alternative HTML version deleted]]

Hello: In the example below (or for a censored data) using survfit.coxph, can anyone point me to a link or a pdf as to how the probabilities appearing in bold under "summary(pred$surv)" are calculated? Do these represent acumulative probability distribution in time (not including censored time)? Thanks very much, parmee *fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*

Hello! I have used read.csv to read in a data frame, and there are a few variables in it, however, when I tried is.list(data$V1) >FALSE In fact, I have tried, they are not vectors either. I'm wondering: 1. What objects are these "lists" of data? 2. How could I find out about the type/inheritence of an object in general? 3. The reason I want it to be a list or vector, is that I

Dear all, I would have some questions on the coxph function for survival analysis, which I use with frailty terms. My model is: mdcox<-coxph(Surv(time,censor)~ gender + age + frailty(area, dist='gauss'), data) I have a very large proportion of censored observations. - If I understand correctly, the function mdcox$frail will return the random effect estimated for each group on the

Hi, I have a coxph model like coxph(Surv(start, stop, censor) ~ x + y, mydata) I would like to calculate the Schoenfeld residuals for the null, i.e the same model where the beta hat vector (in practical terms, the coeff vector spat out by summary()) is constrained to be all 0s --all lese stays the same. I could calculate it by hand, but I was wondering if there is a way of doing it with

Since I read the list in digest form (and was out ill yesterday) I'm late to the discussion. There are 3 steps for predicting survival, using a Cox model: 1. Fit the data fit <- coxph(Surv(time, status) ~ age + ph.ecog, data=lung) The biggest question to answer here is what covariates you wish to base the prediction on. There is the usual tradeoff between too few (leave out something

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 <-

I need to force a coxph() function in R to use a pre-calculated set of beta coefficients of a gene signature consisting of xx genes and the gene expression is also provided of those xx genes. If I try to use "coxph()" function in R using just the gene expression data alone, the beta coefficients and coxph$linear.predictors will change and I need to use the pre-calcuated linear predictor

Hi, In the following results I interpret exp(coef) as the factor that multiplies the base hazard rate if the corresponding variable is TRUE. For example, when the bucket is ks008 and fidelity <= 3, then the rate, compared to the base rate h_0(t), is h(t) = 0.200 h_0(t). My question is then, to what case does the base hazard rate correspond to? I would expect the reference to be the first

Dear All, I am trying to apply the function 'cox.zph' of the library survival, but I receive this error message: not found the object 'residuals.coxph'. I have re-installed the library 'survival' without any change and also a search with RSiteSearch was unsuccessful.. Any suggestion? TIA Giovanni PS: R 2.2.0 Windows XP HE dr. Giovanni Parrinello Section of Medical

Hello everyone, I am searching for a covariate selection procedure in a cox model formulated as a counting process. I use intervals, my formula looks like coxph(Surv(start,stop,status)~ x1+x2+...+cluster(id),robust=T) where id is a country code (I study occurence of civil wars from 1962 to 1997). I'd like something not based on p-values, since they have several flaws for this purpose. I turned

Dear All, Sorry to bother you again. I have a model: coxfita=coxph(Surv(rem.Remtime/365,rem.Rcens)~all.sex,data=nearma) and I'm trying to do a plot of Schoenfeld residuals using the code: plot(cox.zph(coxfita)) abline(h=0,lty=3) The error message I get is: Error in plot.window(...) : need finite 'ylim' values In addition: Warning messages: 1: In sqrt(x$var[i, i] * seval) : NaNs

hi, I applied coxph to my matrix of 300 samples and 215 variables and got the following error Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: Ran out of iterations and did not converge in: fitter(X, Y, strats, offset, init, control, weights = weights, 26% of time data is censored and here