Running ‘testthat.R’ [124s/155s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > 
  > library("testthat")
  > library("sentometrics")
  > 
  > test_check("sentometrics")
  
  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 exponential0.1 exponential0.6 
  
  Aggregate average statistics: 
      mean       sd      max      min meanCorr 
  -0.02282  0.18258  0.73352 -1.18603  0.19341 
  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: 14.53 
   
  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: 0.89 
  Optimal average elastic net lambda parameter: 3.67 
   
  Out-of-sample performance 
  - - - - - - - - - - - - - - - - - - - - 
   
  Mean directional accuracy: 60 % 
  Root mean squared prediction error: 60.25 
  Mean absolute deviation: 45.69
  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: 3889.41 
   
  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: 14.53 
   
  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: 0.89 
  Optimal average elastic net lambda parameter: 3.67 
   
  Out-of-sample performance 
  - - - - - - - - - - - - - - - - - - - - 
   
  Mean directional accuracy: 60 % 
  Root mean squared prediction error: 60.25 
  Mean absolute deviation: 45.69
  A sento_model object. 
  A sento_model object. 
  A sento_model object. 
  A sento_modelIter object. 
  ── 1. Failure: Agreement between sentiment scores on document-level across input
  `test_data` not equal to sentimentList[1:11].
  Component "s1": Column 'word_count': Mean relative difference: 0.00550055
  Component "s2": Column 'word_count': Mean relative difference: 0.00550055
  Component "s3": Column 'word_count': Mean relative difference: 0.00550055
  Component "s4": Column 'word_count': Mean relative difference: 0.00550055
  Component "s5": Column 'word_count': Mean relative difference: 0.00550055
  Component "s6": Column 'word_count': Mean relative difference: 0.00550055
  Component "s7": Column 'word_count': Mean relative difference: 0.00550055
  Component "s8": Column 'word_count': Mean relative difference: 0.00550055
  Component "s10": Column 'word_count': Mean relative difference: 0.00550055
  ...
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  [ OK: 217 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 1 ]
  1. Failure: Agreement between sentiment scores on document-level across input objects (@test_sentiment_computation.R#125) 
  
  Error: testthat unit tests failed
  Execution halted