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Last updated on 2026-10-24 17:51:28 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.3-32 | 4.56 | 107.47 | 112.03 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.3-32 | 4.61 | 64.39 | 69.00 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.3-32 | 75.15 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 1.3-32 | 75.29 | OK | |||
| r-devel-windows-x86_64 | 1.3-32 | 10.00 | 133.00 | 143.00 | OK | |
| r-patched-linux-x86_64 | 1.3-32 | 7.83 | 84.55 | 92.38 | OK | |
| r-release-linux-x86_64 | 1.3-32 | 4.35 | 85.29 | 89.64 | OK | |
| r-release-macos-arm64 | 1.3-32 | 2.00 | 20.00 | 22.00 | OK | |
| r-release-macos-x86_64 | 1.3-32 | 5.00 | 94.00 | 99.00 | OK | |
| r-release-windows-x86_64 | 1.3-32 | 11.00 | 117.00 | 128.00 | OK | |
| r-oldrel-macos-arm64 | 1.3-32 | OK | ||||
| r-oldrel-macos-x86_64 | 1.3-32 | 5.00 | 92.00 | 97.00 | OK | |
| r-oldrel-windows-x86_64 | 1.3-32 | 14.00 | 127.00 | 141.00 | OK |
Version: 1.3-32
Check: examples
Result: ERROR
Running examples in ‘boot-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: censboot
> ### Title: Bootstrap for Censored Data
> ### Aliases: censboot cens.return
> ### Keywords: survival
>
> ### ** Examples
>
> library(survival)
Attaching package: ‘survival’
The following object is 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.
> data(aml, package = "boot") # not the version in survival.
> 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 in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing 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$group,
+ F.surv = aml.s1, G.surv = aml.s2, sim = "cond")
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
>
>
> # For the weird bootstrap we must redefine our function slightly since
> # the data will not contain the group number.
> aml.fun1 <- function(data, str) {
+ surv <- survfit(Surv(data[, 1], data[, 2]) ~ str)
+ out <- NULL
+ st <- 1
+ for (s in 1:length(surv$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)
+ }
+ out
+ }
> aml.wei <- censboot(cbind(aml$time, aml$cens), aml.fun1, R = 499,
+ strata = aml$group, F.surv = aml.s1, sim = "weird")
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
Warning in min(surv$time[inds[1 - surv$surv[inds] >= 0.5]]) :
no non-missing arguments to min; returning Inf
>
> # Now for an example where a cox regression model has been fitted
> # the data we will look at the melanoma data of Example 7.6 from
> # Davison and Hinkley (1997). The fitted model assumes that there
> # is a different survival distribution for the ulcerated and
> # non-ulcerated groups but that the thickness of the tumour has a
> # common effect. We will also assume that the censoring distribution
> # is different in different age groups. The statistic of interest
> # is the linear predictor. This is returned as the values at a
> # number of equally spaced points in the range of interest.
> data(melanoma, package = "boot")
> library(splines)# for ns
> mel.cox <- coxph(Surv(time, status == 1) ~ ns(thickness, df=4) + strata(ulcer),
+ data = melanoma)
> mel.surv <- survfit(mel.cox)
Error in a$specials[[i]] : subscript out of bounds
Calls: survfit ... survfit.coxph -> model.matrix.coxph -> delete.response -> off
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
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