R version 4.5.0 beta (2025-04-02 r88102 ucrt) -- "How About a Twenty-Six" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > > 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: 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 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. [ FAIL 0 | WARN 3 | SKIP 0 | PASS 217 ] [ FAIL 0 | WARN 3 | SKIP 0 | PASS 217 ] > > > proc.time() user system elapsed 50.12 3.06 57.70