Last updated on 2026-10-24 09:50:54 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.9-16 | 10.99 | 127.74 | 138.73 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.9-16 | 8.44 | 89.57 | 98.01 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.9-16 | 10.00 | 110.60 | 120.60 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 0.9-16 | 92.69 | OK | |||
| r-devel-windows-x86_64 | 0.9-16 | 17.00 | 215.00 | 232.00 | OK | |
| r-patched-linux-x86_64 | 0.9-16 | 11.11 | 113.21 | 124.32 | OK | |
| r-release-linux-x86_64 | 0.9-16 | 10.18 | 111.49 | 121.67 | OK | |
| r-release-macos-arm64 | 0.9-16 | 3.00 | 34.00 | 37.00 | OK | |
| r-release-macos-x86_64 | 0.9-16 | 9.00 | 130.00 | 139.00 | OK | |
| r-release-windows-x86_64 | 0.9-16 | 17.00 | 146.00 | 163.00 | OK | |
| r-oldrel-macos-arm64 | 0.9-16 | 3.00 | 36.00 | 39.00 | OK | |
| r-oldrel-macos-x86_64 | 0.9-16 | 9.00 | 118.00 | 127.00 | OK | |
| r-oldrel-windows-x86_64 | 0.9-16 | 21.00 | 169.00 | 190.00 | OK |
Version: 0.9-16
Check: examples
Result: ERROR
Running examples in ‘ipred-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: bagging
> ### Title: Bagging Classification, Regression and Survival Trees
> ### Aliases: bagging ipredbagg ipredbagg.factor ipredbagg.integer
> ### ipredbagg.numeric ipredbagg.Surv ipredbagg.default bagging.data.frame
> ### bagging.default
> ### Keywords: tree
>
> ### ** Examples
>
>
> library("MASS")
> library("survival")
>
> # Classification: Breast Cancer data
>
> data("BreastCancer", package = "mlbench")
>
> # Test set error bagging (nbagg = 50): 3.7% (Breiman, 1998, Table 5)
>
> mod <- bagging(Class ~ Cl.thickness + Cell.size
+ + Cell.shape + Marg.adhesion
+ + Epith.c.size + Bare.nuclei
+ + Bl.cromatin + Normal.nucleoli
+ + Mitoses, data=BreastCancer, coob=TRUE)
> print(mod)
Bagging classification trees with 25 bootstrap replications
Call: bagging.data.frame(formula = Class ~ Cl.thickness + Cell.size +
Cell.shape + Marg.adhesion + Epith.c.size + Bare.nuclei +
Bl.cromatin + Normal.nucleoli + Mitoses, data = BreastCancer,
coob = TRUE)
Out-of-bag estimate of misclassification error: 0.0381
>
> # Test set error bagging (nbagg=50): 7.9% (Breiman, 1996a, Table 2)
> data("Ionosphere", package = "mlbench")
> Ionosphere$V2 <- NULL # constant within groups
>
> bagging(Class ~ ., data=Ionosphere, coob=TRUE)
Bagging classification trees with 25 bootstrap replications
Call: bagging.data.frame(formula = Class ~ ., data = Ionosphere, coob = TRUE)
Out-of-bag estimate of misclassification error: 0.0969
>
> # Double-Bagging: combine LDA and classification trees
>
> # predict returns the linear discriminant values, i.e. linear combinations
> # of the original predictors
>
> comb.lda <- list(list(model=lda, predict=function(obj, newdata)
+ predict(obj, newdata)$x))
>
> # Note: out-of-bag estimator is not available in this situation, use
> # errorest
>
> mod <- bagging(Class ~ ., data=Ionosphere, comb=comb.lda)
>
> predict(mod, Ionosphere[1:10,])
[1] good bad good bad good bad good bad good bad
Levels: bad good
>
> # Regression:
>
> data("BostonHousing", package = "mlbench")
>
> # Test set error (nbagg=25, trees pruned): 3.41 (Breiman, 1996a, Table 8)
>
> mod <- bagging(medv ~ ., data=BostonHousing, coob=TRUE)
> print(mod)
Bagging regression trees with 25 bootstrap replications
Call: bagging.data.frame(formula = medv ~ ., data = BostonHousing,
coob = TRUE)
Out-of-bag estimate of root mean squared error: 4.0892
>
> library("mlbench")
> learn <- as.data.frame(mlbench.friedman1(200))
>
> # Test set error (nbagg=25, trees pruned): 2.47 (Breiman, 1996a, Table 8)
>
> mod <- bagging(y ~ ., data=learn, coob=TRUE)
> print(mod)
Bagging regression trees with 25 bootstrap replications
Call: bagging.data.frame(formula = y ~ ., data = learn, coob = TRUE)
Out-of-bag estimate of root mean squared error: 2.8923
>
> # Survival data
>
> # Brier score for censored data estimated by
> # 10 times 10-fold cross-validation: 0.2 (Hothorn et al,
> # 2002)
>
> data("DLBCL", package = "ipred")
> mod <- bagging(Surv(time,cens) ~ MGEc.1 + MGEc.2 + MGEc.3 + MGEc.4 + MGEc.5 +
+ MGEc.6 + MGEc.7 + MGEc.8 + MGEc.9 +
+ MGEc.10 + IPI, data=DLBCL, coob=TRUE)
Error in a$specials[[i]] : subscript out of bounds
Calls: bagging ... EventHistory.frame -> model.design -> delete.response -> off
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.9-16
Check: tests
Result: ERROR
Running ‘ipred-bugs.R’ [2s/3s]
Comparing ‘ipred-bugs.Rout’ to ‘ipred-bugs.Rout.save’ ... OK
Running ‘ipred-segfault.R’ [4s/4s]
Running ‘ipred-smalltest.R’ [2s/2s]
Comparing ‘ipred-smalltest.Rout’ to ‘ipred-smalltest.Rout.save’ ... OK
Running the tests in ‘tests/ipred-segfault.R’ failed.
Complete output:
> library("ipred")
> library("mlbench")
> library("MASS")
> library("survival")
>
> suppressWarnings(RNGversion("3.5.3"))
>
> actversion <- paste(R.version$major, R.version$minor, sep=".")
> thisversion <- "1.7.0"
>
> #if (compareVersion(actversion, thisversion) >= 0) {
> # RNGversion("1.6.2")
> #}
> set.seed(29081975)
>
>
> # Classification
>
> learn <- as.data.frame(mlbench.twonorm(200))
> test <- as.data.frame(mlbench.twonorm(100))
>
> # bagging
>
> mod <- bagging(classes ~ ., data=learn, coob=TRUE, nbagg=10)
> mod
Bagging classification trees with 10 bootstrap replications
Call: bagging.data.frame(formula = classes ~ ., data = learn, coob = TRUE,
nbagg = 10)
Out-of-bag estimate of misclassification error: 0.195
> predict(mod)[1:10]
[1] 1 1 1 2 2 2 1 1 1 1
Levels: 1 2
>
> # Double-Bagging
>
> comb.lda <- list(list(model=lda, predict=function(obj, newdata)
+ predict(obj, newdata)$x))
>
> mod <- bagging(classes ~ ., data=learn, comb=comb.lda, nbagg=10)
> mod
Bagging classification trees with 10 bootstrap replications
Call: bagging.data.frame(formula = classes ~ ., data = learn, comb = comb.lda,
nbagg = 10)
> predict(mod, newdata=test[1:10,])
[1] 1 1 1 1 1 2 1 2 1 2
Levels: 1 2
> predict(mod, newdata=test[1:10,], agg="aver")
[1] 1 1 1 1 2 2 1 2 1 2
Levels: 1 2
> predict(mod, newdata=test[1:10,], agg="wei")
[1] 1 1 1 1 2 2 1 2 1 2
Levels: 1 2
> predict(mod, newdata=test[1:10,], type="prob")
1 2
[1,] 1.0 0.0
[2,] 0.9 0.1
[3,] 1.0 0.0
[4,] 1.0 0.0
[5,] 0.5 0.5
[6,] 0.0 1.0
[7,] 1.0 0.0
[8,] 0.1 0.9
[9,] 1.0 0.0
[10,] 0.0 1.0
> predict(mod, newdata=test[1:10,], type="prob", agg="aver")
1 2
[1,] 1.0 0.0
[2,] 0.9 0.1
[3,] 1.0 0.0
[4,] 1.0 0.0
[5,] 0.5 0.5
[6,] 0.0 1.0
[7,] 1.0 0.0
[8,] 0.1 0.9
[9,] 1.0 0.0
[10,] 0.0 1.0
> predict(mod, newdata=test[1:10,], type="prob", agg="wei")
1 2
[1,] 1.000000000 0.000000000
[2,] 0.996441281 0.003558719
[3,] 1.000000000 0.000000000
[4,] 1.000000000 0.000000000
[5,] 0.484359233 0.515640767
[6,] 0.000000000 1.000000000
[7,] 1.000000000 0.000000000
[8,] 0.001138952 0.998861048
[9,] 1.000000000 0.000000000
[10,] 0.000000000 1.000000000
>
> mypredict.lda <- function(object, newdata)
+ predict(object, newdata = newdata)$class
>
> errorest(classes ~ ., data=learn, model=lda, predict=mypredict.lda)
Call:
errorest.data.frame(formula = classes ~ ., data = learn, model = lda,
predict = mypredict.lda)
10-fold cross-validation estimator of misclassification error
Misclassification error: 0.035
> errorest(classes ~ ., data=learn, model=lda, predict=mypredict.lda,
+ est.para=control.errorest(k=5, random=FALSE))
Call:
errorest.data.frame(formula = classes ~ ., data = learn, model = lda,
predict = mypredict.lda, est.para = control.errorest(k = 5,
random = FALSE))
5-fold cross-validation estimator of misclassification error
Misclassification error: 0.04
>
> lapply(errorest(classes ~ ., data=learn, model=lda, predict=mypredict.lda,
+ est.para=control.errorest(k=5, random=FALSE, getmodels=TRUE))$models, class)
[[1]]
[1] "lda"
[[2]]
[1] "lda"
[[3]]
[1] "lda"
[[4]]
[1] "lda"
[[5]]
[1] "lda"
> errorest(classes ~ ., data=learn, model=bagging,
+ est.para=control.errorest(k=2), nbagg=10)
Call:
errorest.data.frame(formula = classes ~ ., data = learn, model = bagging,
est.para = control.errorest(k = 2), nbagg = 10)
2-fold cross-validation estimator of misclassification error
Misclassification error: 0.12
> errorest(classes ~ ., data=learn, model=bagging,
+ est.para=control.errorest(k=2), nbagg=10, comb=comb.lda)
Call:
errorest.data.frame(formula = classes ~ ., data = learn, model = bagging,
est.para = control.errorest(k = 2), nbagg = 10, comb = comb.lda)
2-fold cross-validation estimator of misclassification error
Misclassification error: 0.055
> errorest(classes ~ ., data=learn, model=lda,
+ predict=mypredict.lda, estimator="boot")
Call:
errorest.data.frame(formula = classes ~ ., data = learn, model = lda,
predict = mypredict.lda, estimator = "boot")
Bootstrap estimator of misclassification error
with 25 bootstrap replications
Misclassification error: 0.038
Standard deviation: 0.0023
> errorest(classes ~ ., data=learn, model=lda,
+ predict=mypredict.lda, estimator="632plus")
Call:
errorest.data.frame(formula = classes ~ ., data = learn, model = lda,
predict = mypredict.lda, estimator = "632plus")
.632+ Bootstrap estimator of misclassification error
with 25 bootstrap replications
Misclassification error: 0.0336
>
> # Regression
>
> learn <- as.data.frame(mlbench.friedman1(100))
> test <- as.data.frame(mlbench.friedman1(100))
>
> # bagging
>
> mod <- bagging(y ~ ., data=learn, coob=TRUE, nbagg=10)
> mod
Bagging regression trees with 10 bootstrap replications
Call: bagging.data.frame(formula = y ~ ., data = learn, coob = TRUE,
nbagg = 10)
Out-of-bag estimate of root mean squared error: 3.0773
> predict(mod)[1:10]
[1] 13.367299 15.465575 10.728200 21.426540 16.379882 16.992572 NA
[8] 12.899667 8.096002 16.070858
>
> predict(mod, newdata=test[1:10,])
[1] 13.90686 14.94293 12.94732 11.67369 16.63664 14.42252 16.01717 12.34594
[9] 11.96764 22.14124
> predict(mod, newdata=test[1:10,], agg="aver")
[1] 13.90686 14.94293 12.94732 11.67369 16.63664 14.42252 16.01717 12.34594
[9] 11.96764 22.14124
> predict(mod, newdata=test[1:10,], agg="wei")
[1] 13.96527 14.95040 12.92685 11.61045 16.74963 14.59937 16.46226 12.44494
[9] 12.20556 22.00779
> errorest(y ~ ., data=learn, model=lm)
Call:
errorest.data.frame(formula = y ~ ., data = learn, model = lm)
10-fold cross-validation estimator of root mean squared error
Root mean squared error: 2.7385
> errorest(y ~ ., data=learn, model=lm,
+ est.para=control.errorest(k=5, random=FALSE))
Call:
errorest.data.frame(formula = y ~ ., data = learn, model = lm,
est.para = control.errorest(k = 5, random = FALSE))
5-fold cross-validation estimator of root mean squared error
Root mean squared error: 2.7941
> lapply(errorest(y ~ ., data=learn, model=lm,
+ est.para=control.errorest(k=5, random=FALSE, getmodels=TRUE))$models, class)
[[1]]
[1] "lm"
[[2]]
[1] "lm"
[[3]]
[1] "lm"
[[4]]
[1] "lm"
[[5]]
[1] "lm"
> errorest(y ~ ., data=learn, model=lm, estimator="boot")
Call:
errorest.data.frame(formula = y ~ ., data = learn, model = lm,
estimator = "boot")
Bootstrap estimator of root mean squared error
with 25 bootstrap replications
Root mean squared error: 2.7966
>
> # survival
>
> learn <- rsurv(100, model="C")
> test <- rsurv(100, model="C")
>
> mod <- bagging(Surv(time, cens) ~ ., data=learn, nbagg=10)
> mod
Bagging survival trees with 10 bootstrap replications
Call: bagging.data.frame(formula = Surv(time, cens) ~ ., data = learn,
nbagg = 10)
> predict(mod, newdata=test[1:10,])
[[1]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 117 117 0.0751 0.064 0.123
[[2]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 134 134 0.028 0.0247 0.0321
[[3]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 142 142 0.215 0.179 0.241
[[4]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 131 131 0.0216 0.0205 0.028
[[5]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 145 145 0.217 0.168 0.251
[[6]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 105 105 0.0476 0.0421 0.0496
[[7]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 125 125 0.181 0.156 0.218
[[8]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 139 139 0.179 0.156 0.217
[[9]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 131 131 0.181 0.179 0.251
[[10]]
Call: survfit(formula = Surv(agglsample[[j]], aggcens[[j]]) ~ 1)
n events median 0.95LCL 0.95UCL
[1,] 120 120 0.028 0.0247 0.0395
>
> #errorest(Surv(time, cens) ~ ., data=learn, model=bagging,
> # est.para=list(k=2, random=FALSE), nbagg=5)
> #errorest(Surv(time, cens) ~ ., data=learn, model=bagging,
> # estimator="boot", nbagg=5, est.para=list(nboot=5))
> #insert control.errorest
> errorest(Surv(time, cens) ~ ., data=learn, model=bagging,
+ est.para=control.errorest(k=2, random=FALSE), nbagg=5)
Error in a$specials[[i]] : subscript out of bounds
Calls: errorest ... EventHistory.frame -> model.design -> delete.response -> off
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc