> On Nov 5, 2015, at 6:34 AM, Luke Gaylor <luke.gay...@live.com.au> wrote: > > Hello there, > > I have registered for both through nabble and R-help-request with this email. > > I want to post the following question: > > I want to implement a Hosmer Lemeshow Goodness of Fit test to my survival > analysis. > In R we can use the hoslem.test() function. > The x values are our observations, and y are our fitted probabilities. > > So we can take the following data: > > s <- Surv(ovarian$futime, ovarian$fustat) > sWei <- survreg(s ~ age,dist='weibull',data=ovarian) > > so here we assume a weibull distribution. I want to do a HL GOF test to see, > if this is an invalid assumption. > Now how do we get our predicted probabilities. I assume it is with the > predict() function. > I have tried the code, but it is not correct
(It would be better to post the reason for your conclusion of “incorrectitude”. Error? Unexpected result? ) > predict(sWei, newdata=list(ovarian$age), type = 'response’) This code had an unmatched single quote because the flanking character was a”smart-quote. I’m side-stepping the wisdom of using an HL GOF test of a distrubutional assumption. I suspect that the argument to newdata will not have a name. Try: predict(sWei, newdata=list(age = ovarian$age), type = ‘response') Or, since you jsut want the original data to be the basis: predict(sWei, type = ‘response') — David. > > [[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. David Winsemius Alameda, CA, USA ______________________________________________ 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.