Dear Jurgen, > fit <- survfit(mod.allison.5, newdata.1, individual=TRUE) > fit Call: survfit(formula = mod.allison.5, newdata = newdata.1, individual = TRUE)
records n.max n.start events median 0.95LCL 0.95UCL 19809 432 432 114 NA NA NA plot(fit) # shows one survival curve See ?survfit.coxph. Best, John On Sat, 16 Jun 2012 16:23:41 +0200 Jürgen Biedermann <juergen.biederm...@googlemail.com> wrote: > Dear John, > > Thank you very much for the quick answer. I will use the mentioned citation > of your book in the future. > > However: > The Id argument is necessary for the function to know that the different rows > belong to one individual. > > Or how the Help-File says: > > id: > optional variable name of subject identifiers. If this is present, then each > group > of rows with the same subject id represents the covariate path through time of > a single subject, and the result will contain one curve per subject. If the > coxph > fit had strata then that must also be specified in newdata. If missing, then > each > individual row of newdata is presumed to represent a distinct subject and > there > will be nrow(newdata) times the number of strata curves in the result (one for > each strata/subject combination). > > I tried your proposal anyway, but, as expected, fifty different survival > curves appeared. > > So, there is still no solution. > > Best regards > Jürgen > > -- > ----------------------------------- > Jürgen Biedermann > Bergmannstraße 3 > 10961 Berlin-Kreuzberg > Mobil: +49 176 247 54 354 > Home: +49 30 940 53 044 > e-mail: juergen.biederm...@gmail.com > > > --------- Korrespondenz ---------- > > Betreff: Re: How to specify "newdata" in a Cox-Modell with a time dependent > interaction term? > Von: John Fox <j...@mcmaster.ca> > An: J?rgen Biedermann <juergen.biederm...@googlemail.com> > Datum: 16.06.2012 15:55 > > Dear Jürgen, > > > > All the values of your Id variable are equal to 1; how can that make sense? > > Removing the argument Id=id may get you what you want. > > > > By the way, the document to which you refer was an appendix to a 2002 book > > that has been superseded by Fox and Weisberg, An R Companion to Applied > > Regression, Second Edition (Sage, 2011). Appendices for that book are > > available > > at<http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/appendix.html>. > > > > I hope this helps, > > John > > > > ------------------------------------------------ > > John Fox > > Sen. William McMaster Prof. of Social Statistics > > Department of Sociology > > McMaster University > > Hamilton, Ontario, Canada > > http://socserv.mcmaster.ca/jfox/ > > > > On Sat, 16 Jun 2012 15:04:48 +0200 > > Jürgen Biedermann<juergen.biederm...@googlemail.com> wrote: > >> Dear Mr. Therneau, Mr. Fox, or to whoever, who has some time... > >> > >> I don't find a solution to use the "survfit" function (package: survival) > >> for a defined pattern of covariates with a Cox-Model including a time > >> dependent interaction term. Somehow the definition of my "newdata" > >> argument seems to be erroneous. > >> I already googled the problem, found many persons having the same or a > >> similar problem, but still no solution. > >> I want to stress that my time-dependent covariate does not depend on the > >> failure of an individual (in this case it doesn't seem sensible to predict > >> a survivor function for an individual). Rather one of my effects declines > >> with time (time-dependent coefficient). > >> > >> For illustration, I use the example of John Fox's paper "Cox Proportional > >> - Hazards Regression for Survival Data". > >> http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf > >> > >> Do you know any help? See code below > >> > >> Thanks very much in advance > >> Jürgen Biedermann > >> > >> #---------------------------------------- > >> #Code > >> > >> Rossi<- > >> read.table("http://cran.r-project.org/doc/contrib/Fox-Companion/Rossi.txt";, > >> header=T) > >> > >> Rossi.2<- fold(Rossi, time='week', > >> event='arrest', cov=11:62, cov.names='employed') > >> > >> # see below for the fold function from John Fox > >> > >> # modeling an interaction with time (Page 14) > >> > >> mod.allison.5<- coxph(Surv(start, stop, arrest.time) ~ > >> fin + age + age:stop + prio, > >> data=Rossi.2) > >> mod.allison.5 > >> > >> # Attempt to get the survivor function of a person with age=30, fin=0 and > >> prio=5 > >> > >> newdata.1<- > >> data.frame(unique(Rossi.2[c("start","stop")]),fin=0,age=30,prio=5,Id=1,arrest.time=0) > >> fit<- survfit(mod.allison.5,newdata.1,id="Id") > >> > >> Error message: > >> > >> >Fehler in model.frame.default(data = newdata.1, id = "Id", formula = > >> Surv(start, : > >> Variablenlängen sind unterschiedlich (gefunden für '(id)') > >> > >> --> failure, length of variables are different. > >> > >> #----------------------------------------------------------------- > >> fold<- function(data, time, event, cov, > >> cov.names=paste('covariate', '.', 1:ncovs, sep=""), > >> suffix='.time', cov.times=0:ncov, common.times=TRUE, lag=0){ > >> vlag<- function(x, lag) c(rep(NA, lag), x[1:(length(x)-lag)]) > >> xlag<- function(x, lag) apply(as.matrix(x), 2, vlag, lag=lag) > >> all.cov<- unlist(cov) > >> if (!is.list(cov)) cov<- list(cov) > >> ncovs<- length(cov) > >> nrow<- nrow(data) > >> ncol<- ncol(data) > >> ncov<- length(cov[[1]]) > >> nobs<- nrow*ncov > >> if (length(unique(c(sapply(cov, length), length(cov.times)-1)))> 1) > >> stop(paste( > >> "all elements of cov must be of the same length and \n", > >> "cov.times must have one more entry than each element of > >> cov.")) > >> var.names<- names(data) > >> subjects<- rownames(data) > >> omit.cols<- if (!common.times) c(all.cov, cov.times) else all.cov > >> keep.cols<- (1:ncol)[-omit.cols] > >> nkeep<- length(keep.cols) > >> if (is.numeric(event)) event<- var.names[event] > >> times<- if (common.times) matrix(cov.times, nrow, ncov+1, byrow=T) > >> else data[, cov.times] > >> new.data<- matrix(Inf, nobs, 3 + ncovs + nkeep) > >> rownames<- rep("", nobs) > >> colnames(new.data)<- c('start', 'stop', paste(event, suffix, sep=""), > >> var.names[-omit.cols], cov.names) > >> end.row<- 0 > >> for (i in 1:nrow){ > >> start.row<- end.row + 1 > >> end.row<- end.row + ncov > >> start<- times[i, 1:ncov] > >> stop<- times[i, 2:(ncov+1)] > >> event.time<- ifelse (stop == data[i, time]& data[i, event] == > >> 1, 1, 0) > >> keep<- matrix(unlist(data[i, -omit.cols]), ncov, nkeep, byrow=T) > >> select<- apply(matrix(!is.na(data[i, all.cov]), ncol=ncovs), > >> 1, all) > >> rows<- start.row:end.row > >> cov.mat<- xlag(matrix(unlist(data[i, all.cov]), > >> nrow=length(rows)), lag) > >> new.data[rows[select], ]<- > >> cbind(start, stop, event.time, keep, cov.mat)[select,] > >> rownames[rows]<- paste(subjects[i], '.', seq(along=rows), sep="") > >> } > >> row.names(new.data)<- rownames > >> as.data.frame(new.data[new.data[, 1] != Inf& > >> apply(as.matrix(!is.na(new.data[, cov.names])), 1, all), ]) > >> } > >> #----------------------------------------------------------------- > >> ______________________________________________ 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.
