Hi Allen, Thanks for your response. Regarding the issue that Portnoy and PH estimators may not compute parameters in the tail, I believe this is dependent on the amount of censoring (e.g., in the extreme case, if all observations are censored in the upper tail, one cannot obtain estimates in the absence of parametric assumptions). Furthermore, it could not explain the problem I encountered, since other fitting methods did not provide what I needed, nor was able to obtain parameter estimates at the desired quantile (0.9th) in the absence of censoring.
Dr Koenker offered a simple solution, which was to replace the last line with: summary(q2, taus = 1:9/10) instead of requesting the desired taus in the crq() function. Regards, Ashley Ashley I Naimi, PhD Assistant Professor Department Obstetrics and Gynecology McGill University ——————————— web: www.ashleyisaacnaimi.com<http://www.ashleyisaacnaimi.com> email: ashley.na...@mcgill.ca<mailto:ashley.na...@mcgill.ca> phone: 514.574.8946 fax: 514.834.1678 ----------------------- On Feb 6, 2015, at 2:51 PM, Allen Bingham <aebingh...@gmail.com<mailto:aebingh...@gmail.com>> wrote: Ashley, Looking at the code for the crq function it appears that with method="Portnoy" the tau value is not passed to the fitting function (crq.fit.por) and that function appears (I didn't delve into the code to make sure of this ...) to calculate the grid of tau values given the nature of the data, formula, etc. --- --- more telling though is the following snippet I copied from the documentation of the crq function, specifically it states (in part) [I've split apart the pertinent section first]: "... Both the Portnoy and Peng-Huang estimators may be unable to compute estimates of the conditional quantile parameters in the upper tail of distribution. ... Like the Kaplan-Meier estimator, when censoring is heavy in the upper tail the estimated distribution is defective and quantiles are only estimable on a sub-interval of (0,1). The Peng and Huang estimator can be viewed as a generalization of the Nelson Aalen estimator of the cumulative hazard function, and can be formulated as a variant of the conventional quantile regression dual problem. See Koenker (2008) for further details. This paper is available from the package with vignette("crq"). ... " ... so I'm guessing that using one of the other fitting methods may be get you what you need --- HOWEVER I'm only looking at the underlying code and documentation for same --- I'm completely ignorant of the statistical methods you are trying to implement --- and whether or not one of the other fitting methods is appropriate to your analyses. Regardless it seems to me (IMHO) that the documentation for the crq function should indicate that the "tau" or alternatively the "taus" supplied to the crq function are only used with a subset of the method values. Hope this helps-Allen ______________________________________ Allen Bingham Bingham Statistical Consulting aebingh...@gmail.com<mailto:aebingh...@gmail.com> This message and any attachments may contain confidential or privileged information and are only for the use of the intended recipient of this message. If you are not the intended recipient, please notify the sender by return email, and delete or destroy this and all copies of this message and all attachments. Any unauthorized disclosure, use, distribution, or reproduction of this message or any attachments without permission from the sender is prohibited and may be unlawful. -----Original Message----- From: Ashley Isaac Naimi, Mr [mailto:ashley.na...@mcgill.ca] Sent: Tuesday, January 20, 2015 7:52 AM To: r-help@r-project.org Subject: [R] censored quantile regression Hi there, I've generated the following data: library(quantreg) library(survival) set.seed(789) N <- 2000 u <- runif(N) x1 <- rbinom(N,1,.5) x2 <- rbinom(N,1,.5) x1x2<-x1*x2 lambda <- 1 + 1.5*x1 + 1.5*x2 + .5*x1x2 k <- 2 y <- lambda*((-log(1-u))^(1/k));max(y) c <- runif(N,max=15) event = as.numeric(y<=c) mean(event);table(event) cens <- 1-event table(cens);mean(cens) time <-as.matrix(ifelse(event==1,y,c)) St<-Surv(time,event,type="right") To which I've fit the following censored quantile regression model: q2 <- crq(St~x1 + x2 + x1x2,tau=.9,method="Portnoy") summary(q2) As one can see, I'm interested in the 0.9th quantile. But summary(q2) returns only the 20th to 80th percentiles (by 20). How can I get only the 0.9th quantile (aka the 90th percentile)?? All I actually need is the parameter estimate for the interaction (x1x2) for the 90th percentile. I know how to extract it if I were able to obtain estimates at tau=0.9, but no luck. Even though I request the 90th percentile in crq (i.e., "tau=0.9"), the summary function keeps returning the same set of (unwanted) percentiles (20th, 40th, 60th, 80th). I’m using R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet”, Platform: x86_64-apple-darwin13.4.0 (64-bit). Cheers, Ashley I Naimi [[alternative HTML version deleted]] [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
