Last updated on 2026-10-24 01:51:37 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.5.5 | 6.57 | 74.84 | 81.41 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 0.5.5 | 5.59 | 51.02 | 56.61 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.5.5 | 76.82 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 0.5.5 | 52.40 | OK | |||
| r-devel-windows-x86_64 | 0.5.5 | 8.00 | 96.00 | 104.00 | OK | |
| r-patched-linux-x86_64 | 0.5.5 | 6.86 | 69.41 | 76.27 | OK | |
| r-release-linux-x86_64 | 0.5.5 | 6.72 | 69.11 | 75.83 | OK | |
| r-release-macos-arm64 | 0.5.5 | 2.00 | 20.00 | 22.00 | OK | |
| r-release-macos-x86_64 | 0.5.5 | 5.00 | 86.00 | 91.00 | OK | |
| r-release-windows-x86_64 | 0.5.5 | 9.00 | 85.00 | 94.00 | OK | |
| r-oldrel-macos-arm64 | 0.5.5 | OK | ||||
| r-oldrel-macos-x86_64 | 0.5.5 | 4.00 | 79.00 | 83.00 | OK | |
| r-oldrel-windows-x86_64 | 0.5.5 | 10.00 | 106.00 | 116.00 | OK |
Version: 0.5.5
Check: CRAN incoming feasibility
Result: NOTE
Maintainer: ‘Arthur Allignol <arthur.allignol@gmail.com>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: person(given = "Arthur",
family = "Allignol",
role = c("aut", "cre"),
email = "arthur.allignol@gmail.com")
as necessary.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 0.5.5
Check: examples
Result: ERROR
Running examples in ‘kmi-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: cox.kmi
> ### Title: Cox proportional hazards model applied to imputed data sets
> ### Aliases: cox.kmi
> ### Keywords: survival models regression
>
> ### ** Examples
>
> data(icu.pneu)
>
>
> if (require(survival)) {
+
+ set.seed(1313)
+ imp.dat <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
+ etype = event, id = id, failcode = 2, nimp = 5)
+
+ fit.kmi <- cox.kmi(Surv(start, stop, event == 2) ~ pneu, imp.dat)
+
+ summary(fit.kmi)
+
+ ### Now using the censoring-complete data
+ fit <- coxph(Surv(start, adm.cens.exit, event == 2) ~ pneu, icu.pneu)
+
+ summary(fit)
+
+ ## estimation of the censoring distribution adjusted on covariates
+ dat.cova <- kmi(Surv(start, stop, status) ~ age + sex,
+ data = icu.pneu, etype = event, id = id,
+ failcode = 2, nimp = 5)
+
+ fit.kmi2 <- cox.kmi(Surv(start, adm.cens.exit, event == 2) ~ pneu + age,
+ dat.cova)
+
+ summary(fit.kmi2)
+ }
Loading required package: survival
Error in a$specials[[i]] : subscript out of bounds
Calls: kmi ... survfit.coxph -> model.matrix.coxph -> delete.response -> off
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.5.5
Check: tests
Result: ERROR
Running ‘test.kmi.R’ [3s/3s]
Running the tests in ‘tests/test.kmi.R’ failed.
Complete output:
> require(survival)
Loading required package: survival
> require(kmi)
Loading required package: kmi
>
> ### test 1
>
> id <- 1:10
> time <- 1:10
> ev <- c(0, 0, 0, rep(1, 3), rep(2, 4))
> data <- data.frame(id, time, ev)
>
> aa <- kmi(Surv(time, ev != 0) ~ 1, data, etype = ev)
>
> bb <- aa$imputed.data
> test1 <- lapply(bb, function(x) {
+ x[aa$original.data$id %in% 7:10, ]
+ })
> test1 <- do.call(rbind, test1)
>
> all(test1[, 1] == 11)
[1] TRUE
> all(test1[, 2] == 0)
[1] TRUE
>
> aa <- kmi(Surv(time, ev != 0) ~ 1, data, etype = ev, failcode = 2)
>
> bb <- aa$imputed.data
> test1 <- lapply(bb, function(x) {
+ x[aa$original.data$id %in% 4:6, ]
+ })
> test1 <- do.call(rbind, test1)
>
> all(test1[, 1] == 11)
[1] TRUE
> all(test1[, 2] == 0)
[1] TRUE
>
> aa <- kmi(Surv(time, ev != 0) ~ 1, data, etype = ev, epsilon = 2)
>
> bb <- aa$imputed.data
> test1 <- lapply(bb, function(x) {
+ x[aa$original.data$id %in% 7:10, ]
+ })
> test1 <- do.call(rbind, test1)
>
> all(test1[, 1] == 12)
[1] TRUE
> all(test1[, 2] == 0)
[1] TRUE
>
> aa <- kmi(Surv(time, ev != 0) ~ 1, data, etype = ev, nimp = 13)
> length(aa$imputed.data) == 13
[1] TRUE
>
>
> ### test 2
>
> set.seed(198740)
> time <- rexp(100)
> ev <- sample(c(0, 1, 2), 100, replace = TRUE)
> cov <- rbinom(100, 1, 0.5)
> dd <- data.frame(time, ev, cov)
>
> ## add a test when etype is a factor
> dd$status <- factor(ifelse(dd$ev == 0, "cens", ifelse(dd$ev == 1, "rel", "dc")))
>
> set.seed(1440293)
> dat.kmi <- kmi(Surv(time, ev != 0) ~ 1, dd, etype = ev, nimp = 5)
> set.seed(1440293)
> dat.kmi.fact <- kmi(Surv(time, status != "cens") ~ 1, dd, etype = status,
+ nimp = 5, failcode = "rel")
> set.seed(1440293)
> dat.kmi.mixed1 <- kmi(Surv(time, ev != 0) ~ 1, dd, etype = status,
+ nimp = 5, failcode = "rel")
> set.seed(1440293)
> dat.kmi.mixed2 <- kmi(Surv(time, status != "cens") ~ 1, dd, etype = ev,
+ nimp = 5, failcode = 1)
>
>
> fit.kmi <- cox.kmi(Surv(time, ev == 1) ~ cov, dat.kmi)
> fit.kmi.fact <- cox.kmi(Surv(time, status == "rel") ~ cov, dat.kmi)
> fit.kmi.mixed1 <- cox.kmi(Surv(time, status == "rel") ~ cov, dat.kmi.mixed1)
> fit.kmi.mixed2 <- cox.kmi(Surv(time, ev == 1) ~ cov, dat.kmi.mixed2)
>
> all.equal(coef(fit.kmi), coef(fit.kmi.fact))
[1] TRUE
> all.equal(coef(fit.kmi), coef(fit.kmi.mixed1))
[1] TRUE
> all.equal(coef(fit.kmi), coef(fit.kmi.mixed2))
[1] TRUE
> all.equal(fit.kmi$variance, fit.kmi.fact$variance)
[1] TRUE
> all.equal(fit.kmi$variance, fit.kmi.mixed1$variance)
[1] TRUE
> all.equal(fit.kmi$variance, fit.kmi.mixed2$variance)
[1] TRUE
>
> ## fit.kmi
>
> ## summary(fit.kmi)
>
> ## fit.kmi.fact
>
> ## summary(fit.kmi.fact)
>
> ## avec bootstrap
>
> set.seed(867988)
> dat.kmib <- kmi(Surv(time, ev != 0) ~ 1, dd, etype = ev, nimp = 5,
+ boot = TRUE, nboot = 5)
> set.seed(867988)
> dat.kmib.fact <- kmi(Surv(time, ev != 0) ~ 1, dd, etype = status, nimp = 5,
+ boot = TRUE, nboot = 5, failcode = "rel")
>
> fit.kmib <- cox.kmi(Surv(time, ev == 1) ~ cov, dat.kmib)
> fit.kmib.fact <- cox.kmi(Surv(time, status == "rel") ~ cov, dat.kmib.fact)
>
> all.equal(coef(fit.kmib), coef(fit.kmib.fact))
[1] TRUE
>
> fit.kmib
Call:
cox.kmi(formula = Surv(time, ev == 1) ~ cov, imp.data = dat.kmib)
*****************
Pooled estimates:
*****************
coef exp(coef) se(coef) t p
cov -0.07328941 0.9293318 0.3126316 -0.2344274 0.8146571
> fit.kmib.fact
Call:
cox.kmi(formula = Surv(time, status == "rel") ~ cov, imp.data = dat.kmib.fact)
*****************
Pooled estimates:
*****************
coef exp(coef) se(coef) t p
cov -0.07328941 0.9293318 0.3126316 -0.2344274 0.8146571
>
> summary(fit.kmib)
Call:
cox.kmi(formula = Surv(time, ev == 1) ~ cov, imp.data = dat.kmib)
*****************
Pooled estimates:
*****************
coef exp(coef) se(coef) t Pr(>|t|)
cov -0.07329 0.92933 0.31263 -0.234 0.815
exp(coef) exp(-coef) lower .95 upper .95
cov 0.9293 1.076 0.5035 1.715
>
>
> ### test 4
>
> data(icu.pneu)
>
> set.seed(1313)
> dat <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
+ etype = event, id= id, failcode = 2, nimp = 5)
>
> ### add a factor for testing purposes
> icu.pneu$ev.fact <- factor(ifelse(icu.pneu$event == 3, "disch", "death"))
>
> icu.pneu$ev <- icu.pneu$event
> icu.pneu$ev[icu.pneu$status == 0] <- 0
>
> set.seed(1313)
> dat2 <- kmi(Surv(start, stop, ev != 0) ~ 1, data = icu.pneu,
+ etype = ev, id= id, failcode = 2, nimp = 5)
>
> set.seed(1313)
> dat3 <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
+ etype = ev.fact, id = id, failcode = "death", nimp = 5)
>
> a <- logical(5)
> for (i in 1:5) a[i] <- all.equal(dat$imputed.data[[i]][, 1], dat2$imputed.data[[i]][, 1])
> a
[1] TRUE TRUE TRUE TRUE TRUE
>
> fit.kmi <- cox.kmi(Surv(start, stop, event == 2) ~ pneu, dat)
>
> fit.kmi2 <- cox.kmi(Surv(start, stop, ev == 2) ~ pneu, dat2)
>
> fit.kmi3 <- cox.kmi(Surv(start, stop, ev.fact == "death") ~ pneu, dat3)
>
> all.equal(fit.kmi$coefficients, fit.kmi2$coefficients)
[1] TRUE
> all.equal(coef(fit.kmi), coef(fit.kmi3))
[1] TRUE
> all.equal(fit.kmi$variance, fit.kmi2$variance)
[1] TRUE
>
> fit.kmi
Call:
cox.kmi(formula = Surv(start, stop, event == 2) ~ pneu, imp.data = dat)
*****************
Pooled estimates:
*****************
coef exp(coef) se(coef) t p
pneu1 1.10382 3.015664 0.2398608 4.60192 4.186147e-06
>
> fit.kmi2
Call:
cox.kmi(formula = Surv(start, stop, ev == 2) ~ pneu, imp.data = dat2)
*****************
Pooled estimates:
*****************
coef exp(coef) se(coef) t p
pneu1 1.10382 3.015664 0.2398608 4.60192 4.186147e-06
>
> fit.kmi3
Call:
cox.kmi(formula = Surv(start, stop, ev.fact == "death") ~ pneu,
imp.data = dat3)
*****************
Pooled estimates:
*****************
coef exp(coef) se(coef) t p
pneu1 1.10382 3.015664 0.2398608 4.60192 4.186147e-06
>
> ## avec bootstrap
>
> set.seed(598085)
> dat <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
+ etype = event, id= id, failcode = 2, nimp = 5,
+ boot = TRUE, nboot = 5)
>
> set.seed(598085)
> dat2 <- kmi(Surv(start, stop, ev != 0) ~ 1, data = icu.pneu,
+ etype = ev, id= id, failcode = 2, nimp = 5,
+ boot = TRUE, nboot = 5)
>
> set.seed(598085)
> dat3 <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
+ etype = ev.fact, id = id, failcode = "death", nimp = 5,
+ boot = TRUE, nboot = 5)
>
>
> a <- logical(5)
> for (i in 1:5) a[i] <- all.equal(dat$imputed.data[[i]][, 1], dat2$imputed.data[[i]][, 1])
> a
[1] TRUE TRUE TRUE TRUE TRUE
>
> fit.kmi <- cox.kmi(Surv(start, stop, event == 2) ~ pneu, dat)
>
> fit.kmi2 <- cox.kmi(Surv(start, stop, ev == 2) ~ pneu, dat2)
>
> fit.kmi3 <- cox.kmi(Surv(start, stop, ev.fact == "death") ~ pneu, dat3)
>
> all.equal(fit.kmi$coefficients, fit.kmi2$coefficients)
[1] TRUE
> all.equal(coef(fit.kmi), coef(fit.kmi3))
[1] TRUE
> all.equal(fit.kmi$variance, fit.kmi2$variance)
[1] TRUE
>
> fit.kmi
Call:
cox.kmi(formula = Surv(start, stop, event == 2) ~ pneu, imp.data = dat)
*****************
Pooled estimates:
*****************
coef exp(coef) se(coef) t p
pneu1 1.104148 3.016652 0.2399367 4.601829 4.188064e-06
>
> fit.kmi2
Call:
cox.kmi(formula = Surv(start, stop, ev == 2) ~ pneu, imp.data = dat2)
*****************
Pooled estimates:
*****************
coef exp(coef) se(coef) t p
pneu1 1.104148 3.016652 0.2399367 4.601829 4.188064e-06
>
> fit.kmi3
Call:
cox.kmi(formula = Surv(start, stop, ev.fact == "death") ~ pneu,
imp.data = dat3)
*****************
Pooled estimates:
*****************
coef exp(coef) se(coef) t p
pneu1 1.104148 3.016652 0.2399367 4.601829 4.188064e-06
>
> ### with covariates
> ## classic
> set.seed(1)
> dd$juhu <- rnorm(nrow(dd))
>
> set.seed(78223)
> imp.dd <- kmi(Surv(time, ev != 0) ~ juhu, dd,
+ etype = ev, nimp = 5)
Error in a$specials[[i]] : subscript out of bounds
Calls: kmi ... survfit.coxph -> model.matrix.coxph -> delete.response -> off
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc