Running ‘testthat.R’ [115s/141s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > 
  > library("testthat")
  > library("sentometrics")
  > 
  > test_check("sentometrics")
  ── ERROR (test_aggregation.R:81:3): Output for peak documents extraction in line
  Error: cannot xtfrm 'x'
  Backtrace:
       █
    1. ├─testthat::expect_length(peakdocs(s1, n = 7, type = "both"), 7) test_aggregation.R:81:2
    2. │ └─testthat::quasi_label(enquo(object), arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─sentometrics::peakdocs(s1, n = 7, type = "both")
    5.   └─base::order(s, decreasing = ifelse(type == "neg", FALSE, TRUE))
    6.     └─base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
    7.       └─base:::FUN(X[[i]], ...)
    8.         ├─base::as.vector(xtfrm(x))
    9.         ├─base::xtfrm(x)
   10.         └─base::xtfrm.default(x)
  
  ── ERROR (test_aggregation.R:89:3): Output for peak dates extraction in line wit
  Error: cannot xtfrm 'x'
  Backtrace:
       █
    1. ├─testthat::expect_length(...) test_aggregation.R:89:2
    2. │ └─testthat::quasi_label(enquo(object), arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─sentometrics::peakdates(sentMeas1, n = 15, type = "both")
    5.   └─base::order(...)
    6.     └─base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
    7.       └─base:::FUN(X[[i]], ...)
    8.         ├─base::as.vector(xtfrm(x))
    9.         ├─base::xtfrm(x)
   10.         └─base::xtfrm.default(x)
  
  
  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: counts 
  Following scheme is applied for aggregation across documents: proportional 
  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
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  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. 
  ══ testthat results  ═══════════════════════════════════════════════════════════
  ERROR (test_aggregation.R:81:3): Output for peak documents extraction in line with input
  ERROR (test_aggregation.R:89:3): Output for peak dates extraction in line with input
  
  [ FAIL 2 | WARN 0 | SKIP 0 | PASS 209 ]
  Error: Test failures
  Execution halted