Last updated on 2026-07-23 08:49:50 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.99-7 | 27.55 | 280.66 | 308.21 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.99-7 | 17.91 | 189.30 | 207.21 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.99-7 | 37.00 | 445.19 | 482.19 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 0.99-7 | 17.00 | 187.82 | 204.82 | OK | |
| r-devel-windows-x86_64 | 0.99-7 | 41.00 | 341.00 | 382.00 | OK | |
| r-patched-linux-x86_64 | 0.99-7 | 29.52 | 259.43 | 288.95 | OK | |
| r-release-linux-x86_64 | 0.99-7 | 25.75 | 261.04 | 286.79 | OK | |
| r-release-macos-arm64 | 0.99-7 | 7.00 | 66.00 | 73.00 | OK | |
| r-release-macos-x86_64 | 0.99-7 | 20.00 | 424.00 | 444.00 | OK | |
| r-release-windows-x86_64 | 0.99-7 | 40.00 | 346.00 | 386.00 | OK | |
| r-oldrel-macos-arm64 | 0.99-7 | OK | ||||
| r-oldrel-macos-x86_64 | 0.99-7 | 20.00 | 281.00 | 301.00 | OK | |
| r-oldrel-windows-x86_64 | 0.99-7 | 43.00 | 418.00 | 461.00 | OK |
Version: 0.99-7
Check: tests
Result: ERROR
Running ‘LTS-specials.R’ [0s/1s]
Running ‘MCD-specials.R’ [0s/1s]
Running ‘MT-tst.R’ [4s/6s]
Running ‘NAcoef.R’ [1s/1s]
Comparing ‘NAcoef.Rout’ to ‘NAcoef.Rout.save’ ... OK
Running ‘OGK-ex.R’ [0s/1s]
Comparing ‘OGK-ex.Rout’ to ‘OGK-ex.Rout.save’ ... OK
Running ‘Qn-Sn-plots.R’ [0s/1s]
Running ‘Rsquared.R’ [1s/1s]
Comparing ‘Rsquared.Rout’ to ‘Rsquared.Rout.save’ ... OK
Running ‘binom-ni-small.R’ [0s/1s]
Comparing ‘binom-ni-small.Rout’ to ‘binom-ni-small.Rout.save’ ... OK
Running ‘binom-no-x.R’ [0s/0s]
Running ‘comedian-tst.R’ [0s/1s]
Running ‘exact-fit-categorical.R’ [0s/0s]
Running ‘glmrob-1.R’ [5s/7s]
Running ‘glmrob-specials.R’ [0s/1s]
Running ‘huber-etc.R’ [0s/0s]
Comparing ‘huber-etc.Rout’ to ‘huber-etc.Rout.save’ ... OK
Running ‘large-values.R’ [0s/1s]
Running ‘lmrob-data.R’ [2s/3s]
Running ‘lmrob-ex12.R’ [2s/3s]
Running ‘lmrob-methods.R’ [0s/1s]
Comparing ‘lmrob-methods.Rout’ to ‘lmrob-methods.Rout.save’ ... OK
Running ‘lmrob-psifns.R’ [3s/3s]
Comparing ‘lmrob-psifns.Rout’ to ‘lmrob-psifns.Rout.save’ ... OK
Running ‘m-s-estimator.R’ [1s/2s]
Running ‘mc-etc.R’ [1s/1s]
Running ‘mc-strict.R’ [9s/11s]
Running ‘nlregrob-tst.R’ [8s/9s]
Running ‘nlrob-tst.R’ [2s/3s]
Running ‘poisson-ex.R’ [1s/1s]
Running ‘psi-rho-etc.R’ [1s/1s]
Comparing ‘psi-rho-etc.Rout’ to ‘psi-rho-etc.Rout.save’ ... OK
Running ‘small-sample.R’ [5s/7s]
Comparing ‘small-sample.Rout’ to ‘small-sample.Rout.save’ ... OK
Running ‘subsample.R’ [2s/3s]
Running ‘tlts.R’ [1s/1s]
Comparing ‘tlts.Rout’ to ‘tlts.Rout.save’ ... OK
Running ‘tmcd.R’ [4s/5s]
Running ‘weights.R’ [1s/1s]
Comparing ‘weights.Rout’ to ‘weights.Rout.save’ ... OK
Running ‘wgt-himed-xtra.R’ [2s/3s]
Running ‘wgt-himed.R’ [0s/0s]
Comparing ‘wgt-himed.Rout’ to ‘wgt-himed.Rout.save’ ... OK
Running the tests in ‘tests/MCD-specials.R’ failed.
Complete output:
> #### Test special cases for covMcd()
>
> library(robustbase)
>
> ### 1) p = 1 ----------------------------------------------------
> set.seed(1)
> x <- c(rnorm(50),100, 1e10)
> (r1 <- covMcd(x))
Minimum Covariance Determinant (MCD) estimator approximation.
Method: Univariate Fast MCD(alpha=0.5 ==> h=27); nsamp = 500; (n,k)mini = (300,5)
Call:
covMcd(x = x)
Log(Det.): -2.13
Robust Estimate of Location:
x
0.1922
Robust Estimate of Covariance:
x
x 0.5978
> str(r1)
List of 15
$ call : language covMcd(x = x)
$ nsamp : num 500
$ method : chr "Univariate Fast MCD(alpha=0.5 ==> h=27); nsamp = 500; (n,k)mini = (300,5)"
$ cov : num [1, 1] 0.598
..- attr(*, "dimnames")=List of 2
.. ..$ : chr "x"
.. ..$ : chr "x"
$ center : Named num 0.192
..- attr(*, "names")= chr "x"
$ n.obs : int 52
$ alpha : num 0.5
$ quan : num 27
$ raw.cov : num [1, 1] 0.839
..- attr(*, "dimnames")=List of 2
.. ..$ : chr "x"
.. ..$ : chr "x"
$ raw.center: Named num 0.325
..- attr(*, "names")= chr "x"
$ crit : num -2.13
$ mcd.wt : num [1:52] 1 1 1 1 1 1 1 1 1 1 ...
$ X : num [1:52, 1] -0.626 0.184 -0.836 1.595 0.33 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:52] "1" "2" "3" "4" ...
.. ..$ : NULL
$ raw.cnp2 : num [1:2] 6.45 1.14
$ cnp2 : num [1:2] 1.17 1.01
- attr(*, "class")= chr "mcd"
> summary(r1)
Minimum Covariance Determinant (MCD) estimator approximation.
Method: Univariate Fast MCD(alpha=0.5 ==> h=27); nsamp = 500; (n,k)mini = (300,5)
Call:
covMcd(x = x)
Log(Det.): -2.13
Robust Estimate of Location:
x
0.1922
Robust Estimate of Covariance:
x
x 0.5978
Eigenvalues:
[1] 0.5978
Robustness weights:
4 observations c(14,24,51,52) are outliers with |weight| = 0 ( < 0.0019);
48 weights are ~= 1.
> ## with alpha = 1
> (r1.1 <- covMcd(x, alpha = 1))
Minimum Covariance Determinant (MCD) estimator approximation.
Method: MCD(alpha=1 ==> h=52)
alpha = 1: The minimum covariance determinant estimates based on 52 observations
are equal to the classical estimates.
Call:
covMcd(x = x, alpha = 1)
Log(Det.): 42.1
Robust Estimate of Location:
x
2.059
Robust Estimate of Covariance:
x
x 223.9
> str(r1.1)
List of 15
$ call : language covMcd(x = x, alpha = 1)
$ nsamp : num 500
$ method : chr "MCD(alpha=1 ==> h=52) \nalpha = 1: The minimum covariance determinant estimates based on 52 observations \nare "| __truncated__
$ cov : num [1, 1] 224
..- attr(*, "dimnames")=List of 2
.. ..$ : chr "x"
.. ..$ : chr "x"
$ center : Named num 2.06
..- attr(*, "names")= chr "x"
$ n.obs : int 52
$ alpha : num 1
$ quan : num 52
$ raw.cov : num [1, 1] 1.92e+18
..- attr(*, "dimnames")=List of 2
.. ..$ : chr "x"
.. ..$ : chr "x"
$ raw.center: Named num 1.92e+08
..- attr(*, "names")= chr "x"
$ crit : num 42.1
$ mcd.wt : num [1:52] 1 1 1 1 1 1 1 1 1 1 ...
$ X : num [1:52, 1] -0.626 0.184 -0.836 1.595 0.33 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:52] "1" "2" "3" "4" ...
.. ..$ : NULL
$ raw.cnp2 : num [1:2] 1 1
$ cnp2 : num [1:2] 1.14 1
- attr(*, "class")= chr "mcd"
> summary(r1.1)
Minimum Covariance Determinant (MCD) estimator approximation.
Method: MCD(alpha=1 ==> h=52)
alpha = 1: The minimum covariance determinant estimates based on 52 observations
are equal to the classical estimates.
Call:
covMcd(x = x, alpha = 1)
Log(Det.): 42.1
Robust Estimate of Location:
x
2.059
Robust Estimate of Covariance:
x
x 223.9
Eigenvalues:
[1] 223.9
Robustness weights:
2 observations c(51,52) are outliers with |weight| = 0 ( < 0.0019);
50 weights are ~= 1.
>
> ### 1b) p = 1, constant scale
> (rc <- covMcd(rep(1,12)))
Minimum Covariance Determinant (MCD) estimator approximation.
Method: Univariate Fast MCD(alpha=0.5 ==> h=7); nsamp = 500; (n,k)mini = (300,5)
Call:
covMcd(x = rep(1, 12))
Initial scale 0 because more than 'h' (=7) observations are identical.
Log(Det.): -Inf
Robust Estimate of Location:
rep(1, 12)
1
Robust Estimate of Covariance:
rep(1, 12)
rep(1, 12) 0
Warning message:
In covMcd(rep(1, 12)) :
Initial scale 0 because more than 'h' (=7) observations are identical.
> str(rc)
List of 16
$ call : language covMcd(x = rep(1, 12))
$ nsamp : num 500
$ method : chr "Univariate Fast MCD(alpha=0.5 ==> h=7); nsamp = 500; (n,k)mini = (300,5)"
$ singularity:List of 2
..$ kind: chr "identicalObs"
..$ q : num 7
$ cov : num [1, 1] 0
..- attr(*, "dimnames")=List of 2
.. ..$ : chr "rep(1, 12)"
.. ..$ : chr "rep(1, 12)"
$ raw.cov : num [1, 1] 0
..- attr(*, "dimnames")=List of 2
.. ..$ : chr "rep(1, 12)"
.. ..$ : chr "rep(1, 12)"
$ center : Named num 1
..- attr(*, "names")= chr "rep(1, 12)"
$ raw.center : Named num 1
..- attr(*, "names")= chr "rep(1, 12)"
$ n.obs : int 12
$ alpha : num 0.5
$ quan : num 7
$ crit : num -Inf
$ mcd.wt : num [1:12] 1 1 1 1 1 1 1 1 1 1 ...
$ X : num [1:12, 1] 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:12] "1" "2" "3" "4" ...
.. ..$ : NULL
$ raw.cnp2 : num [1:2] 4.97 1.41
$ cnp2 : num [1:2] 1 1
- attr(*, "class")= chr "mcd"
> summary(rc)
Minimum Covariance Determinant (MCD) estimator approximation.
Method: Univariate Fast MCD(alpha=0.5 ==> h=7); nsamp = 500; (n,k)mini = (300,5)
Call:
covMcd(x = rep(1, 12))
Initial scale 0 because more than 'h' (=7) observations are identical.
Log(Det.): -Inf
Robust Estimate of Location:
rep(1, 12)
1
Robust Estimate of Covariance:
rep(1, 12)
rep(1, 12) 0
Eigenvalues:
[1] 0
Robustness weights:
All 12 weights are ~= 1.
> ## with alpha = 1
> (rc1 <- covMcd(rep(1,12), alpha = 1))
Minimum Covariance Determinant (MCD) estimator approximation.
Method: MCD(alpha=1 ==> h=12)
alpha = 1: The minimum covariance determinant estimates based on 12 observations
are equal to the classical estimates.
Call:
covMcd(x = rep(1, 12), alpha = 1)
The classical covariance matrix is singular.
Log(Det.): -Inf
Robust Estimate of Location:
rep(1, 12)
1
Robust Estimate of Covariance:
rep(1, 12)
rep(1, 12) 0
> str(rc1)
List of 16
$ call : language covMcd(x = rep(1, 12), alpha = 1)
$ nsamp : num 500
$ method : chr "MCD(alpha=1 ==> h=12) \nalpha = 1: The minimum covariance determinant estimates based on 12 observations \nare "| __truncated__
$ cov : num [1, 1] 0
..- attr(*, "dimnames")=List of 2
.. ..$ : chr "rep(1, 12)"
.. ..$ : chr "rep(1, 12)"
$ center : Named num 1
..- attr(*, "names")= chr "rep(1, 12)"
$ n.obs : int 12
$ singularity:List of 1
..$ kind: chr "classical"
$ alpha : num 1
$ quan : num 12
$ raw.cov : num [1, 1] 0
..- attr(*, "dimnames")=List of 2
.. ..$ : chr "rep(1, 12)"
.. ..$ : chr "rep(1, 12)"
$ raw.center : Named num 1
..- attr(*, "names")= chr "rep(1, 12)"
$ crit : num -Inf
$ mcd.wt : num [1:12] 1 1 1 1 1 1 1 1 1 1 ...
$ X : num [1:12, 1] 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:12] "1" "2" "3" "4" ...
.. ..$ : NULL
$ raw.cnp2 : num [1:2] 1 1
$ cnp2 : num [1:2] 1 1
- attr(*, "class")= chr "mcd"
> summary(rc1)
Minimum Covariance Determinant (MCD) estimator approximation.
Method: MCD(alpha=1 ==> h=12)
alpha = 1: The minimum covariance determinant estimates based on 12 observations
are equal to the classical estimates.
Call:
covMcd(x = rep(1, 12), alpha = 1)
The classical covariance matrix is singular.
Log(Det.): -Inf
Robust Estimate of Location:
rep(1, 12)
1
Robust Estimate of Covariance:
rep(1, 12)
rep(1, 12) 0
Eigenvalues:
[1] 0
Robustness weights:
All 12 weights are ~= 1.
>
> ### 2) constant observations { multivariate scale == 0 } -----------
> (X <- matrix(rep(2*(1:4), 12), nrow = 12, byrow = TRUE))
[,1] [,2] [,3] [,4]
[1,] 2 4 6 8
[2,] 2 4 6 8
[3,] 2 4 6 8
[4,] 2 4 6 8
[5,] 2 4 6 8
[6,] 2 4 6 8
[7,] 2 4 6 8
[8,] 2 4 6 8
[9,] 2 4 6 8
[10,] 2 4 6 8
[11,] 2 4 6 8
[12,] 2 4 6 8
> (rC <- covMcd(X))
Minimum Covariance Determinant (MCD) estimator approximation.
Method: Fast MCD(alpha=0.5 ==> h=8); nsamp = 500; (n,k)mini = (300,5)
Call:
covMcd(x = X)
The covariance matrix of the data is singular.
There are 12 observations (in the entire dataset of 12 obs.) lying on
the hyperplane with equation a_1*(x_i1 - m_1) + ... + a_p*(x_ip - m_p)
= 0 with (m_1, ..., m_p) the mean of these observations and
coefficients a_i from the vector a <- c(1, 0, 0, 0)
Log(Det.): -Inf
Robust Estimate of Location:
[1] 2 4 6 8
Robust Estimate of Covariance:
[,1] [,2] [,3] [,4]
[1,] 0 0 0 0
[2,] 0 0 0 0
[3,] 0 0 0 0
[4,] 0 0 0 0
Warning message:
In covMcd(X) :*** buffer overflow detected ***: terminated
Aborted
Running the tests in ‘tests/subsample.R’ failed.
Complete output:
> ### test subsample
> ### LU decomposition and singular subsamples handling
> require(robustbase)
Loading required package: robustbase
> source(system.file("xtraR/subsample-fns.R", package = "robustbase", mustWork=TRUE))
> ## instead of relying on system.file("test-tools-1.R", package="Matrix"):
> source(system.file("xtraR/test-tools.R", package = "robustbase")) # assert.EQ(), showProc.time() ..
> options(nwarnings = 4e4, warnPartialMatchArgs = FALSE)
>
> cat("doExtras:", doExtras <- robustbase:::doExtras(),"\n")
doExtras: FALSE
> showProc.time()
Time (user system elapsed): 0 0 0.001
>
> A <- rbind(c(0.001, 1),
+ c(1, 2))
> set.seed(11)
> ## IGNORE_RDIFF_BEGIN
> sa <- tstSubsample(A) # (now typically also shows Matrix version ..)
Loading required package: Matrix
_
Version 1.7-5
Date 2026-03-20
file /home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/Matrix/Meta/package.rds
> ## IGNORE_RDIFF_END
> str(sa)
List of 21
$ x : num [1:2, 1:2] 0.001 1 1 2
$ y : num [1:2] -0.591 0.0266
$ n : int 2
$ m : int 2
$ beta : num [1:2] 1.211 -0.592
$ ind_space: int [1:2] 0 1
$ idc : int [1:2] 0 1
$ idr : int [1:2] 1 0
$ lu : num [1:2, 1:2] 1 0.001 2 0.998
$ v : num [1:2] 1 0.998
$ pivot : int 1
$ Dr : num [1:2] 1 0.5
$ Dc : num [1:2] 2 1
$ rowequ : int 0
$ colequ : int 0
$ status : int 0
$ sample : logi FALSE
$ mts : int 0
$ ss : int 1
$ tolinv : num 1e-07
$ solve : logi TRUE
>
> A <- rbind(c(3, 17, 10),
+ c(2, 4, -2),
+ c(6, 18, 12))
> tstSubsample(A)
>
> ## test some random matrix
> set.seed(1002)
> A <- matrix(rnorm(100), 10)
> tstSubsample(A)
>
> ## test singular matrix handling
> A <- rbind(c(1, 0, 0),
+ c(0, 1, 0),
+ c(0, 1, 0),
+ c(0, 0, 1))
> tstSubsample(A)
>
>
> ## test subsample with mts > 0
> data <- data.frame(y = rnorm(9), expand.grid(A = letters[1:3], B = letters[1:3]))
> x <- model.matrix(y ~ ., data)
> y <- data$y
> ## this should produce a warning and return status == 2
> showSys.time(z <- Rsubsample(x, y, mts=2))
Time user system elapsed
Time 0.001 0.000 0.001
Warning message:
In Rsubsample(x, y, mts = 2) :*** buffer overflow detected ***: terminated
Aborted
Flavor: r-devel-linux-x86_64-debian-gcc