Yes, I have checked that previous answers to "Ran out of iterations..." questions do not solve my problem.
I have fault data on Firefox, 899 faults and 1395 (estimated) censored faults. The censoring all happens on one of half a dozen start days and half a dozen end days (the initial/final release of a version).
library(survival) ff_usage=read.csv("http://www.coding-guidelines.com/R_code/ff_usage.csv", as.is=TRUE) f_sur=Surv(ff_usage$start, ff_usage$end, event=ff_usage$event) plot(survfit(f_sur ~ 1)) f_cox=coxph(f_sur ~ total_usage+cluster(fault_id), data=ff_usage)
The Kaplan-Meier curve looks about right.
total_usage is an estimate of the number of Firefox users up until the fault is reported. This is very time dependent and so each fault timeline is broken up into 7 day intervals clustered on
fault_id; unsplit original.
The dependency on
total_usage (or its log) could be close to 1 (I am hoping for one or the other).
I have tried setting
init and increasing
strata(src_id) and subsetting on
Most of the start/end times are estimated and have a regular interval, I have tried adding some randomization, e.g.,
runif(n, -3, 3). No change.
All I ever see is:
Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not converge
Suggestions for things to try welcome.