```r Call: ICC(x = veracity) Intraclass correlation coefficients type ICC F df1 df2 p lower bound upper bound Single_raters_absolute ICC1 0.66 6.8 99 200 1.7e-30 0.56 0.74 Single_random_raters ICC2 0.66 7.4 99 198 1.2e-32 0.56 0.75 Single_fixed_raters ICC3 0.68 7.4 99 198 1.2e-32 0.59 0.76 Average_raters_absolute ICC1k 0.85 6.8 99 200 1.7e-30 0.79 0.90 Average_random_raters ICC2k 0.85 7.4 99 198 1.2e-32 0.79 0.90 Average_fixed_raters ICC3k 0.86 7.4 99 198 1.2e-32 0.81 0.90 Number of subjects = 100 Number of Judges = 3 See the help file for a discussion of the other 4 McGraw and Wong estimates,Correlation computed with • Method: 'pearson' • Missing treated using: 'pairwise.complete.obs' # A tibble: 3 × 4 term veracity_cn veracity_pg veracity_lc <chr> <dbl> <dbl> <dbl> 1 veracity_cn NA 0.706 0.647 2 veracity_pg 0.706 NA 0.717 3 veracity_lc 0.647 0.717 NA Call: ICC(x = classification) Intraclass correlation coefficients type ICC F df1 df2 p lower bound upper bound Single_raters_absolute ICC1 0.46 3.6 99 200 9.8e-15 0.34 0.58 Single_random_raters ICC2 0.48 4.4 99 198 3.7e-19 0.33 0.61 Single_fixed_raters ICC3 0.53 4.4 99 198 3.7e-19 0.42 0.64 Average_raters_absolute ICC1k 0.72 3.6 99 200 9.8e-15 0.61 0.80 Average_random_raters ICC2k 0.73 4.4 99 198 3.7e-19 0.59 0.82 Average_fixed_raters ICC3k 0.77 4.4 99 198 3.7e-19 0.68 0.84 Number of subjects = 100 Number of Judges = 3 See the help file for a discussion of the other 4 McGraw and Wong estimates,Correlation computed with • Method: 'pearson' • Missing treated using: 'pairwise.complete.obs' # A tibble: 3 × 4 term classify_cn classify_pg classify_lc <chr> <dbl> <dbl> <dbl> 1 classify_cn NA 0.474 0.516 2 classify_pg 0.474 NA 0.602 3 classify_lc 0.516 0.602 NA Call: ICC(x = harmful) Intraclass correlation coefficients type ICC F df1 df2 p lower bound upper bound Single_raters_absolute ICC1 0.20 1.7 99 200 5.3e-04 0.076 0.33 Single_random_raters ICC2 0.23 2.0 99 198 1.6e-05 0.105 0.36 Single_fixed_raters ICC3 0.25 2.0 99 198 1.6e-05 0.129 0.38 Average_raters_absolute ICC1k 0.42 1.7 99 200 5.3e-04 0.198 0.60 Average_random_raters ICC2k 0.47 2.0 99 198 1.6e-05 0.260 0.62 Average_fixed_raters ICC3k 0.50 2.0 99 198 1.6e-05 0.307 0.65 Number of subjects = 100 Number of Judges = 3 See the help file for a discussion of the other 4 McGraw and Wong estimates,Correlation computed with • Method: 'pearson' • Missing treated using: 'pairwise.complete.obs' # A tibble: 3 × 4 term harm_cn harm_pg harm_lc <chr> <dbl> <dbl> <dbl> 1 harm_cn NA 0.280 0.346 2 harm_pg 0.280 NA 0.212 3 harm_lc 0.346 0.212 NA Call: ICC(x = mis) Intraclass correlation coefficients type ICC F df1 df2 p lower bound upper bound Single_raters_absolute ICC1 0.60 5.6 99 200 4.0e-25 0.50 0.70 Single_random_raters ICC2 0.61 6.8 99 198 2.3e-30 0.47 0.72 Single_fixed_raters ICC3 0.66 6.8 99 198 2.3e-30 0.56 0.74 Average_raters_absolute ICC1k 0.82 5.6 99 200 4.0e-25 0.75 0.87 Average_random_raters ICC2k 0.83 6.8 99 198 2.3e-30 0.73 0.89 Average_fixed_raters ICC3k 0.85 6.8 99 198 2.3e-30 0.79 0.90 Number of subjects = 100 Number of Judges = 3 See the help file for a discussion of the other 4 McGraw and Wong estimates,Correlation computed with • Method: 'pearson' • Missing treated using: 'pairwise.complete.obs' # A tibble: 3 × 4 term mis_cn mis_pg mis_lc <chr> <dbl> <dbl> <dbl> 1 mis_cn NA 0.651 0.661 2 mis_pg 0.651 NA 0.721 3 mis_lc 0.661 0.721 NA ```