Using R 2.10 on Windows: I have a filtered database of 650k event observations in a data frame with 20+ variables.
I'd like to be able to quickly generate estimate and plot survival curves. However the survfit and cph() functions are extremely slow. As an example: I tried results.cox<-coxph(Surv(duration, success) ~ start_time + factor1+ factor2+ variable3, data=filteredData) #(took a few seconds) plot(results.cox) #(never finished in an hour) I also tried the cph() function, with similar results. Is there some easier quick-and-dirty way of producing and plotting survival curves for large data sets? I've seen some references on this list that suggest that the underlying algorithm is O(numObs * numSuccesses) and could be sped up. Has this been done? Thanks, _______________________________________________ This e-mail may contain information that is confidential, privileged or otherwise protected from disclosure. If you are not an intended recipient of this e-mail, do not duplicate or redistribute it by any means. Please delete it and any attachments and notify the sender that you have received it in error. Unless specifically indicated, this e-mail is not an offer to buy or sell or a solicitation to buy or sell any securities, investment products or other financial product or service, an official confirmation of any transaction, or an official statement of Barclays. Any views or opinions presented are solely those of the author and do not necessarily represent those of Barclays. This e-mail is subject to terms available at the following link: www.barcap.com/emaildisclaimer. By messaging with Barclays you consent to the foregoing. Barclays Capital is the investment banking division of Barclays Bank PLC, a company registered in England (number 1026167) with its registered offi! ce at 1 Churchill Place, London, E14 5HP. This email may relate to or be sent from other members of the Barclays Group. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
