Last updated on 2019-04-22 07:46:41 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.4.8 | 5.09 | 76.68 | 81.77 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.4.8 | 4.34 | 57.65 | 61.99 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.4.8 | 96.44 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.4.8 | 94.48 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 1.4.8 | 15.00 | 88.00 | 103.00 | ERROR | |
| r-patched-linux-x86_64 | 1.4.8 | 5.41 | 75.98 | 81.39 | ERROR | |
| r-patched-solaris-x86 | 1.4.8 | 149.70 | ERROR | |||
| r-release-linux-x86_64 | 1.4.8 | 3.19 | 67.92 | 71.11 | OK | |
| r-release-windows-ix86+x86_64 | 1.4.8 | 5.00 | 106.00 | 111.00 | OK | |
| r-release-osx-x86_64 | 1.4.8 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 1.4.8 | 9.00 | 114.00 | 123.00 | OK | |
| r-oldrel-osx-x86_64 | 1.4.8 | OK |
Version: 1.4.8
Check: tests
Result: ERROR
Running 'run-all.R' [20s/21s]
Running the tests in 'tests/run-all.R' failed.
Complete output:
> library(testthat)
> library(fanovaGraph)
Loading required package: sensitivity
Loading required package: igraph
Attaching package: 'igraph'
The following object is masked from 'package:testthat':
compare
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Loading required package: DiceKriging
>
> test_check("fanovaGraph")
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
-- 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
-60.9438209 not equivalent to ...[].
1/1 mismatches
[1] -60.9 - -85.3 == 24.3
-- 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
0.008837558 not equivalent to ...[].
1/1 mismatches
[1] 0.00884 - 0.00829 == 0.000549
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10
- parameters upper bounds : 2 2 2
- best initial criterion value(s) : 8.196933
N = 3, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= -8.1969 |proj g|= 1.2609
At iterate 1 f = -8.5263 |proj g|= 1.1046
At iterate 2 f = -8.584 |proj g|= 0.5739
At iterate 3 f = -8.6328 |proj g|= 0.81099
At iterate 4 f = -8.7399 |proj g|= 0.84982
At iterate 5 f = -8.7886 |proj g|= 0.24597
At iterate 6 f = -8.7949 |proj g|= 0.055409
At iterate 7 f = -8.7955 |proj g|= 0.18664
At iterate 8 f = -8.7958 |proj g|= 0.013326
At iterate 9 f = -8.7958 |proj g|= 0.00073279
At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
iterations 11
function evaluations 14
segments explored during Cauchy searches 13
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 3.93423e-05
final function value -8.79581
F = -8.79581
final value -8.795814
converged
threshold RMSE
[1,] 0.0 0.6598290
[2,] 0.4 0.2384378
[3,] 1.0 1.0152007
-- 3. Failure: thresholdIdentification works (@tests.R#93) --------------------
c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
3/3 mismatches (average diff: 0.125)
[1] 0.328 - 0.660 == -0.3317
[2] 0.219 - 0.238 == -0.0197
[3] 0.991 - 1.015 == -0.0244
== testthat results ===========================================================
OK: 29 SKIPPED: 0 FAILED: 3
1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
3. Failure: thresholdIdentification works (@tests.R#93)
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.4.8
Check: tests
Result: ERROR
Running ‘run-all.R’ [14s/21s]
Running the tests in ‘tests/run-all.R’ failed.
Complete output:
> library(testthat)
> library(fanovaGraph)
Loading required package: sensitivity
Loading required package: igraph
Attaching package: 'igraph'
The following object is masked from 'package:testthat':
compare
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Loading required package: DiceKriging
>
> test_check("fanovaGraph")
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
-60.9438209 not equivalent to ...[].
1/1 mismatches
[1] -60.9 - -85.3 == 24.3
── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
0.008837558 not equivalent to ...[].
1/1 mismatches
[1] 0.00884 - 0.00829 == 0.000549
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10
- parameters upper bounds : 2 2 2
- best initial criterion value(s) : 8.196933
N = 3, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= -8.1969 |proj g|= 1.2609
At iterate 1 f = -8.5263 |proj g|= 1.1046
At iterate 2 f = -8.584 |proj g|= 0.5739
At iterate 3 f = -8.6328 |proj g|= 0.81099
At iterate 4 f = -8.7399 |proj g|= 0.84982
At iterate 5 f = -8.7886 |proj g|= 0.24597
At iterate 6 f = -8.7949 |proj g|= 0.055409
At iterate 7 f = -8.7955 |proj g|= 0.18664
At iterate 8 f = -8.7958 |proj g|= 0.013326
At iterate 9 f = -8.7958 |proj g|= 0.00073279
At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
iterations 11
function evaluations 14
segments explored during Cauchy searches 13
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 3.93423e-05
final function value -8.79581
F = -8.79581
final value -8.795814
converged
threshold RMSE
[1,] 0.0 0.6598290
[2,] 0.4 0.2384378
[3,] 1.0 1.0152007
── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
3/3 mismatches (average diff: 0.125)
[1] 0.328 - 0.660 == -0.3317
[2] 0.219 - 0.238 == -0.0197
[3] 0.991 - 1.015 == -0.0244
══ testthat results ═══════════════════════════════════════════════════════════
OK: 29 SKIPPED: 0 FAILED: 3
1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
3. Failure: thresholdIdentification works (@tests.R#93)
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.4.8
Check: tests
Result: ERROR
Running ‘run-all.R’ [24s/25s]
Running the tests in ‘tests/run-all.R’ failed.
Complete output:
> library(testthat)
> library(fanovaGraph)
Loading required package: sensitivity
Loading required package: igraph
Attaching package: 'igraph'
The following object is masked from 'package:testthat':
compare
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Loading required package: DiceKriging
>
> test_check("fanovaGraph")
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
-60.9438209 not equivalent to ...[].
1/1 mismatches
[1] -60.9 - -85.3 == 24.3
── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
0.008837558 not equivalent to ...[].
1/1 mismatches
[1] 0.00884 - 0.00829 == 0.000549
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10
- parameters upper bounds : 2 2 2
- best initial criterion value(s) : 8.196933
N = 3, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= -8.1969 |proj g|= 1.2609
At iterate 1 f = -8.5263 |proj g|= 1.1046
At iterate 2 f = -8.584 |proj g|= 0.5739
At iterate 3 f = -8.6328 |proj g|= 0.81099
At iterate 4 f = -8.7399 |proj g|= 0.84982
At iterate 5 f = -8.7886 |proj g|= 0.24597
At iterate 6 f = -8.7949 |proj g|= 0.055409
At iterate 7 f = -8.7955 |proj g|= 0.18664
At iterate 8 f = -8.7958 |proj g|= 0.013326
At iterate 9 f = -8.7958 |proj g|= 0.00073279
At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
iterations 11
function evaluations 14
segments explored during Cauchy searches 13
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 3.93423e-05
final function value -8.79581
F = -8.79581
final value -8.795814
converged
threshold RMSE
[1,] 0.0 0.6598290
[2,] 0.4 0.2384378
[3,] 1.0 1.0152007
── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
3/3 mismatches (average diff: 0.125)
[1] 0.328 - 0.660 == -0.3317
[2] 0.219 - 0.238 == -0.0197
[3] 0.991 - 1.015 == -0.0244
══ testthat results ═══════════════════════════════════════════════════════════
OK: 29 SKIPPED: 0 FAILED: 3
1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
3. Failure: thresholdIdentification works (@tests.R#93)
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.4.8
Check: tests
Result: ERROR
Running ‘run-all.R’ [23s/25s]
Running the tests in ‘tests/run-all.R’ failed.
Complete output:
> library(testthat)
> library(fanovaGraph)
Loading required package: sensitivity
Loading required package: igraph
Attaching package: 'igraph'
The following object is masked from 'package:testthat':
compare
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Loading required package: DiceKriging
>
> test_check("fanovaGraph")
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
-60.9438209 not equivalent to ...[].
1/1 mismatches
[1] -60.9 - -85.3 == 24.3
── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
0.008837558 not equivalent to ...[].
1/1 mismatches
[1] 0.00884 - 0.00829 == 0.000549
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10
- parameters upper bounds : 2 2 2
- best initial criterion value(s) : 8.196933
N = 3, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= -8.1969 |proj g|= 1.2609
At iterate 1 f = -8.5263 |proj g|= 1.1046
At iterate 2 f = -8.584 |proj g|= 0.5739
At iterate 3 f = -8.6328 |proj g|= 0.81099
At iterate 4 f = -8.7399 |proj g|= 0.84982
At iterate 5 f = -8.7886 |proj g|= 0.24597
At iterate 6 f = -8.7949 |proj g|= 0.055409
At iterate 7 f = -8.7955 |proj g|= 0.18664
At iterate 8 f = -8.7958 |proj g|= 0.013326
At iterate 9 f = -8.7958 |proj g|= 0.00073279
At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
iterations 11
function evaluations 14
segments explored during Cauchy searches 13
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 3.93423e-05
final function value -8.79581
F = -8.79581
final value -8.795814
converged
threshold RMSE
[1,] 0.0 0.6598290
[2,] 0.4 0.2384378
[3,] 1.0 1.0152007
── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
3/3 mismatches (average diff: 0.125)
[1] 0.328 - 0.660 == -0.3317
[2] 0.219 - 0.238 == -0.0197
[3] 0.991 - 1.015 == -0.0244
══ testthat results ═══════════════════════════════════════════════════════════
OK: 29 SKIPPED: 0 FAILED: 3
1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
3. Failure: thresholdIdentification works (@tests.R#93)
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 1.4.8
Check: tests
Result: ERROR
Running 'run-all.R' [19s]
Running the tests in 'tests/run-all.R' failed.
Complete output:
> library(testthat)
> library(fanovaGraph)
Loading required package: sensitivity
Loading required package: igraph
Attaching package: 'igraph'
The following object is masked from 'package:testthat':
compare
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Loading required package: DiceKriging
>
> test_check("fanovaGraph")
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
-- 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
-60.9438209 not equivalent to ...[].
1/1 mismatches
[1] -60.9 - -85.3 == 24.3
-- 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
0.008837558 not equivalent to ...[].
1/1 mismatches
[1] 0.00884 - 0.00829 == 0.000549
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10
- parameters upper bounds : 2 2 2
- best initial criterion value(s) : 8.196933
N = 3, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= -8.1969 |proj g|= 1.2609
At iterate 1 f = -8.5263 |proj g|= 1.1046
At iterate 2 f = -8.584 |proj g|= 0.5739
At iterate 3 f = -8.6328 |proj g|= 0.81099
At iterate 4 f = -8.7399 |proj g|= 0.84982
At iterate 5 f = -8.7886 |proj g|= 0.24597
At iterate 6 f = -8.7949 |proj g|= 0.055409
At iterate 7 f = -8.7955 |proj g|= 0.18664
At iterate 8 f = -8.7958 |proj g|= 0.013326
At iterate 9 f = -8.7958 |proj g|= 0.00073279
At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
iterations 11
function evaluations 14
segments explored during Cauchy searches 13
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 3.93423e-05
final function value -8.79581
F = -8.79581
final value -8.795814
converged
threshold RMSE
[1,] 0.0 0.6598290
[2,] 0.4 0.2384378
[3,] 1.0 1.0152007
-- 3. Failure: thresholdIdentification works (@tests.R#93) --------------------
c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
3/3 mismatches (average diff: 0.125)
[1] 0.328 - 0.660 == -0.3317
[2] 0.219 - 0.238 == -0.0197
[3] 0.991 - 1.015 == -0.0244
== testthat results ===========================================================
OK: 29 SKIPPED: 0 FAILED: 3
1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
3. Failure: thresholdIdentification works (@tests.R#93)
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-windows-ix86+x86_64
Version: 1.4.8
Check: tests
Result: ERROR
Running ‘run-all.R’ [19s/21s]
Running the tests in ‘tests/run-all.R’ failed.
Complete output:
> library(testthat)
> library(fanovaGraph)
Loading required package: sensitivity
Loading required package: igraph
Attaching package: 'igraph'
The following object is masked from 'package:testthat':
compare
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Loading required package: DiceKriging
>
> test_check("fanovaGraph")
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
-60.9438209 not equivalent to ...[].
1/1 mismatches
[1] -60.9 - -85.3 == 24.3
── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
0.008837558 not equivalent to ...[].
1/1 mismatches
[1] 0.00884 - 0.00829 == 0.000549
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10
- parameters upper bounds : 2 2 2
- best initial criterion value(s) : 8.196933
N = 3, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= -8.1969 |proj g|= 1.2609
At iterate 1 f = -8.5263 |proj g|= 1.1046
At iterate 2 f = -8.584 |proj g|= 0.5739
At iterate 3 f = -8.6328 |proj g|= 0.81099
At iterate 4 f = -8.7399 |proj g|= 0.84982
At iterate 5 f = -8.7886 |proj g|= 0.24597
At iterate 6 f = -8.7949 |proj g|= 0.055409
At iterate 7 f = -8.7955 |proj g|= 0.18664
At iterate 8 f = -8.7958 |proj g|= 0.013326
At iterate 9 f = -8.7958 |proj g|= 0.00073279
At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
iterations 11
function evaluations 14
segments explored during Cauchy searches 13
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 3.93423e-05
final function value -8.79581
F = -8.79581
final value -8.795814
converged
threshold RMSE
[1,] 0.0 0.6598290
[2,] 0.4 0.2384378
[3,] 1.0 1.0152007
── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
3/3 mismatches (average diff: 0.125)
[1] 0.328 - 0.660 == -0.3317
[2] 0.219 - 0.238 == -0.0197
[3] 0.991 - 1.015 == -0.0244
══ testthat results ═══════════════════════════════════════════════════════════
OK: 29 SKIPPED: 0 FAILED: 3
1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
3. Failure: thresholdIdentification works (@tests.R#93)
Error: testthat unit tests failed
Execution halted
Flavor: r-patched-linux-x86_64
Version: 1.4.8
Check: tests
Result: ERROR
Running ‘run-all.R’ [36s/55s]
Running the tests in ‘tests/run-all.R’ failed.
Complete output:
> library(testthat)
> library(fanovaGraph)
Loading required package: sensitivity
Loading required package: igraph
Attaching package: 'igraph'
The following object is masked from 'package:testthat':
compare
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
Loading required package: DiceKriging
>
> test_check("fanovaGraph")
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
- parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
- best initial criterion value(s) : -26.60656
N = 6, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= 26.607 |proj g|= 3.0822
At iterate 1 f = 20.74 |proj g|= 3.6659
At iterate 2 f = 14.479 |proj g|= 2.4267
At iterate 3 f = 12.836 |proj g|= 0.96942
At iterate 4 f = 12.753 |proj g|= 0.39588
At iterate 5 f = 12.729 |proj g|= 0.20836
At iterate 6 f = 12.724 |proj g|= 0.14199
At iterate 7 f = 12.72 |proj g|= 0.032266
At iterate 8 f = 12.72 |proj g|= 0.013542
At iterate 9 f = 12.72 |proj g|= 0.0062952
At iterate 10 f = 12.72 |proj g|= 0.001052
At iterate 11 f = 12.72 |proj g|= 0.0003525
At iterate 12 f = 12.72 |proj g|= 0.00011112
iterations 12
function evaluations 14
segments explored during Cauchy searches 16
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 0.000111123
final function value 12.7196
F = 12.7196
final value 12.719557
converged
── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
-60.9438209 not equivalent to ...[].
1/1 mismatches
[1] -60.9 - -85.3 == 24.3
── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
0.008837558 not equivalent to ...[].
1/1 mismatches
[1] 0.00884 - 0.00829 == 0.000549
optimisation start
------------------
* estimation method : MLE
* optimisation method : BFGS
* analytical gradient : used
* trend model : ~1
* covariance model :
- type : matern5_2
- nugget : NO
- parameters lower bounds : 1e-10 1e-10 1e-10
- parameters upper bounds : 2 2 2
- best initial criterion value(s) : 8.196933
N = 3, M = 5 machine precision = 2.22045e-16
At X0, 0 variables are exactly at the bounds
At iterate 0 f= -8.1969 |proj g|= 1.2609
At iterate 1 f = -8.5263 |proj g|= 1.1046
At iterate 2 f = -8.584 |proj g|= 0.5739
At iterate 3 f = -8.6328 |proj g|= 0.81099
At iterate 4 f = -8.7399 |proj g|= 0.84982
At iterate 5 f = -8.7886 |proj g|= 0.24597
At iterate 6 f = -8.7949 |proj g|= 0.055409
At iterate 7 f = -8.7955 |proj g|= 0.18664
At iterate 8 f = -8.7958 |proj g|= 0.013326
At iterate 9 f = -8.7958 |proj g|= 0.00073279
At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
iterations 11
function evaluations 14
segments explored during Cauchy searches 13
BFGS updates skipped 0
active bounds at final generalized Cauchy point 0
norm of the final projected gradient 3.93423e-05
final function value -8.79581
F = -8.79581
final value -8.795814
converged
threshold RMSE
[1,] 0.0 0.6598290
[2,] 0.4 0.2384378
[3,] 1.0 1.0152007
── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
3/3 mismatches (average diff: 0.125)
[1] 0.328 - 0.660 == -0.3317
[2] 0.219 - 0.238 == -0.0197
[3] 0.991 - 1.015 == -0.0244
══ testthat results ═══════════════════════════════════════════════════════════
OK: 29 SKIPPED: 0 FAILED: 3
1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
3. Failure: thresholdIdentification works (@tests.R#93)
Error: testthat unit tests failed
Execution halted
Flavor: r-patched-solaris-x86