Last updated on 2018-10-19 01:46:51 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.5.1 | 49.08 | 170.05 | 219.13 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 0.5.1 | 47.33 | 124.70 | 172.03 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 0.5.1 | 273.77 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 0.5.1 | 260.26 | NOTE | |||
| r-devel-windows-ix86+x86_64 | 0.5.1 | 143.00 | 262.00 | 405.00 | NOTE | |
| r-patched-linux-x86_64 | 0.5.1 | 53.82 | 141.93 | 195.75 | NOTE | |
| r-patched-solaris-x86 | 0.5.1 | 326.50 | NOTE | |||
| r-release-linux-x86_64 | 0.5.1 | 54.48 | 141.33 | 195.81 | NOTE | |
| r-release-windows-ix86+x86_64 | 0.5.1 | 146.00 | 330.00 | 476.00 | NOTE | |
| r-release-osx-x86_64 | 0.5.1 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 0.5.1 | 114.00 | 360.00 | 474.00 | ERROR | |
| r-oldrel-osx-x86_64 | 0.5.1 | ERROR |
Version: 0.5.1
Check: for GNU extensions in Makefiles
Result: NOTE
GNU make is a SystemRequirements.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64
Version: 0.5.1
Check: installed package size
Result: NOTE
installed size is 5.3Mb
sub-directories of 1Mb or more:
data 2.3Mb
libs 2.6Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-osx-x86_64
Version: 0.5.1
Check: data for non-ASCII characters
Result: NOTE
Note: found 4436 marked UTF-8 strings
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64
Version: 0.5.1
Check: running tests for arch ‘i386’
Result: ERROR
Running 'testthat.R' [75s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
>
> library("testthat")
> library("sentometrics")
Loading required package: data.table
>
> test_check("sentometrics")
iteration: 1 from 6
alphas run: 0.2, 0.7
iteration: 2 from 6
alphas run: 0.2, 0.7
iteration: 3 from 6
alphas run: 0.2, 0.7
iteration: 4 from 6
alphas run: 0.2, 0.7
iteration: 5 from 6
alphas run: 0.2, 0.7
iteration: 6 from 6
alphas run: 0.2, 0.7
-- 1. Error: (unknown) (@test_methods_sentomeasures.R#33) ---------------------
length of 'center' must equal the number of columns of 'x'
1: scale(sentMeas, center = as.vector(sentMeas$stats["mean", ]), scale = as.vector(sentMeas$stats["sd",
])) at testthat/test_methods_sentomeasures.R:33
2: scale.sentomeasures(sentMeas, center = as.vector(sentMeas$stats["mean", ]), scale = as.vector(sentMeas$stats["sd",
]))
3: scale(measures, center, scale)
4: scale.default(measures, center, scale)
5: stop("length of 'center' must equal the number of columns of 'x'")
alphas run: 0.2, 0.7
alphas run: 0.2, 0.7
alphas run: 0.2, 0.7
Training model... Done.
Training model... Done.
Training model... Done.
alphas run: 0.2, 0.7
iteration: 1 from 16
alphas run: 0, 0.4, 1
iteration: 2 from 16
alphas run: 0, 0.4, 1
iteration: 3 from 16
alphas run: 0, 0.4, 1
iteration: 4 from 16
alphas run: 0, 0.4, 1
iteration: 5 from 16
alphas run: 0, 0.4, 1
iteration: 6 from 16
alphas run: 0, 0.4, 1
iteration: 7 from 16
alphas run: 0, 0.4, 1
iteration: 8 from 16
alphas run: 0, 0.4, 1
iteration: 9 from 16
alphas run: 0, 0.4, 1
iteration: 10 from 16
alphas run: 0, 0.4, 1
iteration: 11 from 16
alphas run: 0, 0.4, 1
iteration: 12 from 16
alphas run: 0, 0.4, 1
iteration: 13 from 16
alphas run: 0, 0.4, 1
iteration: 14 from 16
alphas run: 0, 0.4, 1
iteration: 15 from 16
alphas run: 0, 0.4, 1
iteration: 16 from 16
alphas run: 0, 0.4, 1
iteration: 1 from 16
alphas run: 0, 0.4, 1
iteration: 2 from 16
alphas run: 0, 0.4, 1
iteration: 3 from 16
alphas run: 0, 0.4, 1
iteration: 4 from 16
alphas run: 0, 0.4, 1
iteration: 5 from 16
alphas run: 0, 0.4, 1
iteration: 6 from 16
alphas run: 0, 0.4, 1
iteration: 7 from 16
alphas run: 0, 0.4, 1
iteration: 8 from 16
alphas run: 0, 0.4, 1
iteration: 9 from 16
alphas run: 0, 0.4, 1
iteration: 10 from 16
alphas run: 0, 0.4, 1
iteration: 11 from 16
alphas run: 0, 0.4, 1
iteration: 12 from 16
alphas run: 0, 0.4, 1
iteration: 13 from 16
alphas run: 0, 0.4, 1
iteration: 14 from 16
alphas run: 0, 0.4, 1
iteration: 15 from 16
alphas run: 0, 0.4, 1
iteration: 16 from 16
alphas run: 0, 0.4, 1
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Number of observations: 226
Optimal elastic net alpha parameter: 0.7
Optimal elastic net lambda parameter: 0.11
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) 96.374913
GI_en--economy--almon1 1445.453900
GI_en--noneconomy--almon1 -2747.069670
LM_en--wsj--almon1 -3768.057855
LM_en--wapo--almon1 5157.542215
LM_en--economy--almon1 1510.211743
GI_en--wsj--almon1_inv -242.569023
GI_en--wapo--almon1_inv 1407.865522
LM_en--wsj--almon1_inv 170.561023
LM_en--wapo--almon1_inv -1793.703655
LM_en--economy--almon1_inv 1692.843627
GI_en--economy--almon2 214.657191
GI_en--noneconomy--almon2 -801.010997
LM_en--economy--almon2 1508.768614
LM_en--economy--almon2_inv 409.196574
GI_en--wsj--almon3 -103.845098
GI_en--noneconomy--almon3 -503.717642
LM_en--wsj--almon3 -2018.717050
LM_en--wapo--almon3 423.943608
LM_en--economy--almon3 -4288.401756
GI_en--wsj--almon3_inv 629.186140
GI_en--economy--almon3_inv -3049.207135
GI_en--noneconomy--almon3_inv 3207.076752
LM_en--wsj--almon3_inv 4705.304398
LM_en--wapo--almon3_inv -4942.936279
x1 -2.975119
x2 1.495385
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: binomial
Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
Number of observations: 233
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 100
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) -0.65335663
x1 0.27680048
x2 0.02006744
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: multinomial
Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
Number of observations: 229
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 0.15
Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
- - - - - - - - - - - - - - - - - - - -
below- 7
below 10
above 7
above+ 5
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Sample size: 216
Total number of iterations/predictions: 16
Optimal average elastic net alpha parameter: 1
Optimal average elastic net lambda parameter: 49.91
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 33.33 %
Root mean squared prediction error: 70
Mean absolute deviation: 55.36
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Sample size: 216
Total number of iterations/predictions: 16
Optimal average elastic net alpha parameter: 0
Optimal average elastic net lambda parameter: 4692.05
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 53.33 %
Root mean squared prediction error: 49.7
Mean absolute deviation: 36.93
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Number of observations: 226
Optimal elastic net alpha parameter: 0.7
Optimal elastic net lambda parameter: 0.11
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) 96.374913
GI_en--economy--almon1 1445.453900
GI_en--noneconomy--almon1 -2747.069670
LM_en--wsj--almon1 -3768.057855
LM_en--wapo--almon1 5157.542215
LM_en--economy--almon1 1510.211743
GI_en--wsj--almon1_inv -242.569023
GI_en--wapo--almon1_inv 1407.865522
LM_en--wsj--almon1_inv 170.561023
LM_en--wapo--almon1_inv -1793.703655
LM_en--economy--almon1_inv 1692.843627
GI_en--economy--almon2 214.657191
GI_en--noneconomy--almon2 -801.010997
LM_en--economy--almon2 1508.768614
LM_en--economy--almon2_inv 409.196574
GI_en--wsj--almon3 -103.845098
GI_en--noneconomy--almon3 -503.717642
LM_en--wsj--almon3 -2018.717050
LM_en--wapo--almon3 423.943608
LM_en--economy--almon3 -4288.401756
GI_en--wsj--almon3_inv 629.186140
GI_en--economy--almon3_inv -3049.207135
GI_en--noneconomy--almon3_inv 3207.076752
LM_en--wsj--almon3_inv 4705.304398
LM_en--wapo--almon3_inv -4942.936279
x1 -2.975119
x2 1.495385
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: binomial
Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
Number of observations: 233
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 100
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) -0.65335663
x1 0.27680048
x2 0.02006744
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: multinomial
Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
Number of observations: 229
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 0.15
Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
- - - - - - - - - - - - - - - - - - - -
below- 7
below 10
above 7
above+ 5
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Sample size: 216
Total number of iterations/predictions: 16
Optimal average elastic net alpha parameter: 1
Optimal average elastic net lambda parameter: 49.91
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 33.33 %
Root mean squared prediction error: 70
Mean absolute deviation: 55.36
A sentomodel object.A sentomodel object.A sentomodel object.A sentomodeliter object.== testthat results ===========================================================
OK: 135 SKIPPED: 0 FAILED: 1
1. Error: (unknown) (@test_methods_sentomeasures.R#33)
Error: testthat unit tests failed
Execution halted
Flavor: r-oldrel-windows-ix86+x86_64
Version: 0.5.1
Check: running tests for arch ‘x64’
Result: ERROR
Running 'testthat.R' [85s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
>
> library("testthat")
> library("sentometrics")
Loading required package: data.table
>
> test_check("sentometrics")
iteration: 1 from 6
alphas run: 0.2, 0.7
iteration: 2 from 6
alphas run: 0.2, 0.7
iteration: 3 from 6
alphas run: 0.2, 0.7
iteration: 4 from 6
alphas run: 0.2, 0.7
iteration: 5 from 6
alphas run: 0.2, 0.7
iteration: 6 from 6
alphas run: 0.2, 0.7
-- 1. Error: (unknown) (@test_methods_sentomeasures.R#33) ---------------------
length of 'center' must equal the number of columns of 'x'
1: scale(sentMeas, center = as.vector(sentMeas$stats["mean", ]), scale = as.vector(sentMeas$stats["sd",
])) at testthat/test_methods_sentomeasures.R:33
2: scale.sentomeasures(sentMeas, center = as.vector(sentMeas$stats["mean", ]), scale = as.vector(sentMeas$stats["sd",
]))
3: scale(measures, center, scale)
4: scale.default(measures, center, scale)
5: stop("length of 'center' must equal the number of columns of 'x'")
alphas run: 0.2, 0.7
alphas run: 0.2, 0.7
alphas run: 0.2, 0.7
Training model... Done.
Training model... Done.
Training model... Done.
alphas run: 0.2, 0.7
iteration: 1 from 16
alphas run: 0, 0.4, 1
iteration: 2 from 16
alphas run: 0, 0.4, 1
iteration: 3 from 16
alphas run: 0, 0.4, 1
iteration: 4 from 16
alphas run: 0, 0.4, 1
iteration: 5 from 16
alphas run: 0, 0.4, 1
iteration: 6 from 16
alphas run: 0, 0.4, 1
iteration: 7 from 16
alphas run: 0, 0.4, 1
iteration: 8 from 16
alphas run: 0, 0.4, 1
iteration: 9 from 16
alphas run: 0, 0.4, 1
iteration: 10 from 16
alphas run: 0, 0.4, 1
iteration: 11 from 16
alphas run: 0, 0.4, 1
iteration: 12 from 16
alphas run: 0, 0.4, 1
iteration: 13 from 16
alphas run: 0, 0.4, 1
iteration: 14 from 16
alphas run: 0, 0.4, 1
iteration: 15 from 16
alphas run: 0, 0.4, 1
iteration: 16 from 16
alphas run: 0, 0.4, 1
iteration: 1 from 16
alphas run: 0, 0.4, 1
iteration: 2 from 16
alphas run: 0, 0.4, 1
iteration: 3 from 16
alphas run: 0, 0.4, 1
iteration: 4 from 16
alphas run: 0, 0.4, 1
iteration: 5 from 16
alphas run: 0, 0.4, 1
iteration: 6 from 16
alphas run: 0, 0.4, 1
iteration: 7 from 16
alphas run: 0, 0.4, 1
iteration: 8 from 16
alphas run: 0, 0.4, 1
iteration: 9 from 16
alphas run: 0, 0.4, 1
iteration: 10 from 16
alphas run: 0, 0.4, 1
iteration: 11 from 16
alphas run: 0, 0.4, 1
iteration: 12 from 16
alphas run: 0, 0.4, 1
iteration: 13 from 16
alphas run: 0, 0.4, 1
iteration: 14 from 16
alphas run: 0, 0.4, 1
iteration: 15 from 16
alphas run: 0, 0.4, 1
iteration: 16 from 16
alphas run: 0, 0.4, 1
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Number of observations: 226
Optimal elastic net alpha parameter: 0.7
Optimal elastic net lambda parameter: 0.11
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) 96.374913
GI_en--economy--almon1 1445.453895
GI_en--noneconomy--almon1 -2747.069668
LM_en--wsj--almon1 -3768.057854
LM_en--wapo--almon1 5157.542206
LM_en--economy--almon1 1510.211737
GI_en--wsj--almon1_inv -242.569022
GI_en--wapo--almon1_inv 1407.865521
LM_en--wsj--almon1_inv 170.561023
LM_en--wapo--almon1_inv -1793.703655
LM_en--economy--almon1_inv 1692.843622
GI_en--economy--almon2 214.657189
GI_en--noneconomy--almon2 -801.010995
LM_en--economy--almon2 1508.768623
LM_en--economy--almon2_inv 409.196576
GI_en--wsj--almon3 -103.845099
GI_en--noneconomy--almon3 -503.717641
LM_en--wsj--almon3 -2018.717049
LM_en--wapo--almon3 423.943606
LM_en--economy--almon3 -4288.401757
GI_en--wsj--almon3_inv 629.186139
GI_en--economy--almon3_inv -3049.207127
GI_en--noneconomy--almon3_inv 3207.076749
LM_en--wsj--almon3_inv 4705.304396
LM_en--wapo--almon3_inv -4942.936268
x1 -2.975119
x2 1.495385
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: binomial
Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
Number of observations: 233
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 100
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) -0.65335663
x1 0.27680048
x2 0.02006744
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: multinomial
Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
Number of observations: 229
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 0.15
Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
- - - - - - - - - - - - - - - - - - - -
below- 7
below 10
above 7
above+ 5
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Sample size: 216
Total number of iterations/predictions: 16
Optimal average elastic net alpha parameter: 1
Optimal average elastic net lambda parameter: 49.91
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 33.33 %
Root mean squared prediction error: 70
Mean absolute deviation: 55.36
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Sample size: 216
Total number of iterations/predictions: 16
Optimal average elastic net alpha parameter: 0
Optimal average elastic net lambda parameter: 4692.05
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 53.33 %
Root mean squared prediction error: 49.7
Mean absolute deviation: 36.93
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Number of observations: 226
Optimal elastic net alpha parameter: 0.7
Optimal elastic net lambda parameter: 0.11
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) 96.374913
GI_en--economy--almon1 1445.453895
GI_en--noneconomy--almon1 -2747.069668
LM_en--wsj--almon1 -3768.057854
LM_en--wapo--almon1 5157.542206
LM_en--economy--almon1 1510.211737
GI_en--wsj--almon1_inv -242.569022
GI_en--wapo--almon1_inv 1407.865521
LM_en--wsj--almon1_inv 170.561023
LM_en--wapo--almon1_inv -1793.703655
LM_en--economy--almon1_inv 1692.843622
GI_en--economy--almon2 214.657189
GI_en--noneconomy--almon2 -801.010995
LM_en--economy--almon2 1508.768623
LM_en--economy--almon2_inv 409.196576
GI_en--wsj--almon3 -103.845099
GI_en--noneconomy--almon3 -503.717641
LM_en--wsj--almon3 -2018.717049
LM_en--wapo--almon3 423.943606
LM_en--economy--almon3 -4288.401757
GI_en--wsj--almon3_inv 629.186139
GI_en--economy--almon3_inv -3049.207127
GI_en--noneconomy--almon3_inv 3207.076749
LM_en--wsj--almon3_inv 4705.304396
LM_en--wapo--almon3_inv -4942.936268
x1 -2.975119
x2 1.495385
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: binomial
Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
Number of observations: 233
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 100
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) -0.65335663
x1 0.27680048
x2 0.02006744
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: multinomial
Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
Number of observations: 229
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 0.15
Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
- - - - - - - - - - - - - - - - - - - -
below- 7
below 10
above 7
above+ 5
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Sample size: 216
Total number of iterations/predictions: 16
Optimal average elastic net alpha parameter: 1
Optimal average elastic net lambda parameter: 49.91
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 33.33 %
Root mean squared prediction error: 70
Mean absolute deviation: 55.36
A sentomodel object.A sentomodel object.A sentomodel object.A sentomodeliter object.== testthat results ===========================================================
OK: 135 SKIPPED: 0 FAILED: 1
1. Error: (unknown) (@test_methods_sentomeasures.R#33)
Error: testthat unit tests failed
Execution halted
Flavor: r-oldrel-windows-ix86+x86_64
Version: 0.5.1
Check: tests
Result: ERROR
Running ‘testthat.R’ [61s/54s]
Running the tests in ‘tests/testthat.R’ failed.
Last 13 lines of output:
Optimal average elastic net alpha parameter: 1
Optimal average elastic net lambda parameter: 49.91
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 33.33 %
Root mean squared prediction error: 70
Mean absolute deviation: 55.36
A sentomodel object.A sentomodel object.A sentomodel object.A sentomodeliter object.══ testthat results ═══════════════════════════════════════════════════════════
OK: 135 SKIPPED: 0 FAILED: 1
1. Error: (unknown) (@test_methods_sentomeasures.R#33)
Error: testthat unit tests failed
Execution halted
Flavor: r-oldrel-osx-x86_64