Thanks David for prompt reply. I agree with you. However, I still fail to get the survfit function to work with newdata. In my previous example I changed the column names of testX matrix and I still fail.
> colnames(testX)<-names(coxph.model$coefficients) > sfit<- survfit(coxph.model,newdata=data.frame(testX)) Error in model.frame.default(formula = Surv(trainTime, trainStatus) ~ : variable lengths differ (found for 'trainX') What would be solution in my simple example to get the survival curves for testX? Thanks in advance. DK > CC: r-help@r-project.org > From: dwinsem...@comcast.net > To: dkrsta...@hotmail.com > Subject: Re: [R] survival survfit with newdata > Date: Thu, 17 May 2012 00:52:55 -0400 > > > On May 16, 2012, at 5:08 PM, Damjan Krstajic wrote: > > > > > Dear all, > > > > I am confused with the behaviour of survfit with newdata option. > > Yes. It has the same behavior as any other newdata/predict from > regression. You need to supply a dataframe with the same names as in > the original formula. Doesn't look as though that strategy is being > followed. The name of the column needs to be 'trainX' since that was > what was its name on the RHS of hte formula, and you may want to > specify times. If you fail to follow those rules, the function falls > back on offering estimates from the original data. > > > > > I am using the latest version R-2-15-0. In the simple example below > > I am building a coxph model on 90 patients and trying to predict 10 > > patients. Unfortunately the survival curve at the end is for 90 > > patients. > > As is proper with a malformed newdata argument. > > > Could somebody please from the survival package confirm that this > > behaviour is as expected or not - because I cannot find a way of > > using 'newdata' with really new data. Thanks in advance. DK > > > >> x<-matrix(rnorm(100*20),100,20) > > > >> > > time<-runif(100,min=0,max=7) > > > >> > > status<-sample(c(0,1), 100, replace = TRUE) > >> trainX<-x[11:100,] > >> > > trainTime<-time[11:100] > >> > > trainStatus<-status[11:100] > >> > > testX<-x[1:10,] > >> coxph.model<- > > coxph(Surv(trainTime,trainStatus)~ trainX) > >> sfit<- survfit(coxph.model,newdata=data.frame(testX)) > > > >> > > dim(sfit$surv) > > > > [1] 90 90 > > > > > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > 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. > > David Winsemius, MD > West Hartford, CT > ______________________________________________ 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.
