Package: boot Version: 1.2.23-1 Severity: serious boot fails to build:
> * checking examples ... ERROR
> Running examples in boot-Ex.R failed.
> The error most likely occurred in:
>
> > ### * censboot
> >
> > flush(stderr()); flush(stdout())
> >
> > ### Name: censboot
> > ### Title: Bootstrap for Censored Data
> > ### Aliases: censboot cens.return
> > ### Keywords: survival
> >
> > ### ** Examples
> >
> > data(aml, package="boot")
> > library(survival)
> Loading required package: splines
>
> Attaching package: 'survival'
>
>
> The following object(s) are masked _by_ .GlobalEnv :
>
> aml
>
>
> The following object(s) are masked from package:boot :
>
> aml
>
> > # Example 3.9 of Davison and Hinkley (1997) does a bootstrap on some
> > # remission times for patients with a type of leukaemia. The patients
> > # were divided into those who received maintenance chemotherapy and
> > # those who did not. Here we are interested in the median remission
> > # time for the two groups.
> > aml.fun <- function(data) {
> + surv <- survfit(Surv(time, cens)~group, data=data)
> + out <- NULL
> + st <- 1
> + for (s in 1:length(surv$strata)) {
> + inds <- st:(st+surv$strata[s]-1)
> + md <- min(surv$time[inds[1-surv$surv[inds]>=0.5]])
> + st <- st+surv$strata[s]
> + out <- c(out,md)
> + }
> + out
> + }
> > aml.case <- censboot(aml,aml.fun,R=499,strata=aml$group)
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> >
> > # Now we will look at the same statistic using the conditional
> > # bootstrap and the weird bootstrap. For the conditional bootstrap
> > # the survival distribution is stratified but the censoring
> > # distribution is not.
> >
> > aml.s1 <- survfit(Surv(time,cens)~group, data=aml)
> > aml.s2 <- survfit(Surv(time-0.001*cens,1-cens)~1, data=aml)
> > aml.cond <- censboot(aml,aml.fun,R=499,strata=aml$group,
> + F.surv=aml.s1,G.surv=aml.s2,sim="cond")
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> >
> > # For the weird bootstrap we must redefine our function slightly since
> > # the data will not contain the group number.
> > aml.fun1 <- function(data,str) {
> + surv <- survfit(Surv(data[,1],data[,2])~str)
> + out <- NULL
> + st <- 1
> + for (s in 1:length(surv$strata)) {
> + inds <- st:(st+surv$strata[s]-1)
> + md <- min(surv$time[inds[1-surv$surv[inds]>=0.5]])
> + st <- st+surv$strata[s]
> + out <- c(out,md)
> + }
> + }
> > aml.wei <- censboot(cbind(aml$time,aml$cens),aml.fun1,R=499,
> + strata=aml$group, F.surv=aml.s1,sim="weird")
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> Warning: no finite arguments to min; returning Inf
> >
> > # Now for an example where a cox regression model has been fitted
> > # the data we will look at the melanoma data of Example 7.6 from
> > # Davison and Hinkley (1997). The fitted model assumes that there
> > # is a different survival distribution for the ulcerated and
> > # non-ulcerated groups but that the thickness of the tumour has a
> > # common effect. We will also assume that the censoring distribution
> > # is different in different age groups. The statistic of interest
> > # is the linear predictor. This is returned as the values at a
> > # number of equally spaced points in the range of interest.
> > data(melanoma, package="boot")
> > library(splines)# for ns
> > mel.cox <- coxph(Surv(time,status==1)~ns(thickness,df=4)+strata(ulcer),
> + data=melanoma)
> > mel.surv <- survfit(mel.cox)
> > agec <- cut(melanoma$age,c(0,39,49,59,69,100))
> > mel.cens <- survfit(Surv(time-0.001*(status==1),status!=1)~
> + strata(agec),data=melanoma)
> > mel.fun <- function(d) {
> + t1 <- ns(d$thickness,df=4)
> + cox <- coxph(Surv(d$time,d$status==1) ~ t1+strata(d$ulcer))
> + eta <- unique(cox$linear.predictors)
> + u <- unique(d$thickness)
> + sp <- smooth.spline(u,eta,df=20)
> + th <- seq(from=0.25,to=10,by=0.25)
> + predict(sp,th)$y
> + }
> > mel.str<-cbind(melanoma$ulcer,agec)
> > # this is slow!
> > mel.mod <- censboot(melanoma,mel.fun,R=999,F.surv=mel.surv,
> + G.surv=mel.cens,cox=mel.cox,strata=mel.str,sim="model")
> Error in xy.coords(x, y) : 'x' and 'y' lengths differ
> Execution halted
--
Matt
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