On 30 September 2005 at 13:34, Matt Kraai wrote:
| 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:
Which platform is that, Matt?
I disabled a few of these tests on other packages formerly provided by
r-recommended, and I guess I need to disable it here too.
Dirk
| >
| > > ### * 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
--
Statistics: The (futile) attempt to offer certainty about uncertainty.
-- Roger Koenker, 'Dictionary of Received Ideas of Statistics'
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