CRAN Package Check Results for Package sentometrics

Last updated on 2019-10-20 01:47:02 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.7.0 90.00 236.47 326.47 NOTE
r-devel-linux-x86_64-debian-gcc 0.7.0 65.76 204.95 270.71 NOTE
r-devel-linux-x86_64-fedora-clang 0.7.0 386.31 NOTE
r-devel-linux-x86_64-fedora-gcc 0.7.0 359.74 NOTE
r-devel-windows-ix86+x86_64 0.7.0 176.00 331.00 507.00 NOTE
r-patched-linux-x86_64 0.7.0 71.68 217.65 289.33 NOTE
r-patched-solaris-x86 0.7.0 445.70 ERROR
r-release-linux-x86_64 0.7.0 71.49 217.59 289.08 NOTE
r-release-windows-ix86+x86_64 0.7.0 149.00 354.00 503.00 NOTE
r-release-osx-x86_64 0.7.0 NOTE
r-oldrel-windows-ix86+x86_64 0.7.0 171.00 317.00 488.00 NOTE
r-oldrel-osx-x86_64 0.7.0 NOTE

Check Details

Version: 0.7.0
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: 0.7.0
Check: installed package size
Result: NOTE
     installed size is 7.7Mb
     sub-directories of 1Mb or more:
     data 2.3Mb
     libs 4.5Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+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: 0.7.0
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: 0.7.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [138s/161s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     >
     > library("testthat")
     > library("sentometrics")
     Loading required package: data.table
     >
     > test_check("sentometrics")
     ── 1. Failure: Test input and output of sentiment aggregation function (@test_ag
     all.equal(wc[, 1], wc[, 2]) isn't true.
    
     Iteration: 1 from 9
     alphas run: 0
     Iteration: 2 from 9
     alphas run: 0
     Iteration: 3 from 9
     alphas run: 0
     Iteration: 4 from 9
     alphas run: 0
     Iteration: 5 from 9
     alphas run: 0
     Iteration: 6 from 9
     alphas run: 0
     Iteration: 7 from 9
     alphas run: 0
     Iteration: 8 from 9
     alphas run: 0
     Iteration: 9 from 9
     alphas run: 0
    
     This sento_measures object contains 24 textual sentiment time series with 7237 observations each (daily).
    
     Following features are present: wsj wapo economy noneconomy
     Following lexicons are used to calculate sentiment: HENRY_en LM_en
     Following scheme is applied for aggregation within documents:
     Following scheme is applied for aggregation across documents:
     Following schemes are applied for aggregation across time: linear exponential_0.1 exponential_0.6
    
     Aggregate average statistics:
     mean sd max min meanCorr
     -0.02278 0.35316 2.88894 -3.83737 0.13423
     A sento_measures object (24 textual sentiment time series, 7237 observations).
     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: 0.2
     Optimal elastic net lambda parameter: 14.52
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) 105.249163
     x1 5.404962
     x2 -1.393848
     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) -0.60098780
     x1 -0.05540991
     x2 0.24513464
     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: 0.7
     Optimal elastic net lambda parameter: 0.02
    
     Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
     - - - - - - - - - - - - - - - - - - - -
    
     below- 8
     below 6
     above 8
     above+ 6
     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: 50.51
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 60 %
     Root mean squared prediction error: 59.94
     Mean absolute deviation: 44.35
     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: 3889.32
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 26.67 %
     Root mean squared prediction error: 44.32
     Mean absolute deviation: 29.73
     Model specification
     - - - - - - - - - - - - - - - - - - - -
    
     Model type: gaussian
     Calibration: via Cp information criterion
     Number of observations: 226
     Optimal elastic net alpha parameter: 0.2
     Optimal elastic net lambda parameter: 14.52
    
     Non-zero coefficients
     - - - - - - - - - - - - - - - - - - - -
    
     (Intercept) 105.249163
     x1 5.404962
     x2 -1.393848
     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) -0.60098780
     x1 -0.05540991
     x2 0.24513464
     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: 0.7
     Optimal elastic net lambda parameter: 0.02
    
     Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
     - - - - - - - - - - - - - - - - - - - -
    
     below- 8
     below 6
     above 8
     above+ 6
     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: 50.51
    
     Out-of-sample performance
     - - - - - - - - - - - - - - - - - - - -
    
     Mean directional accuracy: 60 %
     Root mean squared prediction error: 59.94
     Mean absolute deviation: 44.35
     A sento_model object.
     A sento_model object.
     A sento_model object.
     A sento_modelIter object.
     ── 2. Failure: Agreement between sentiment scores on sentence-level across input
     all(...) isn't true.
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 186 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 2 ]
     1. Failure: Test input and output of sentiment aggregation function (@test_aggregation.R#64)
     2. Failure: Agreement between sentiment scores on sentence-level across input objects (@test_sentiment_computation.R#108)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-solaris-x86