Print Comparison Results
Usage
# S3 method for class 'tna_comparison'
print(x, ...)See also
Model comparison functions
compare(),
compare.group_tna(),
plot.tna_comparison(),
plot_compare(),
plot_compare.group_tna()
Examples
model_x <- tna(group_regulation[1:200, ])
model_y <- tna(group_regulation[1001:1200, ])
comp <- compare(model_x, model_y)
print(comp)
#> Edge difference metrics
#> # A tibble: 81 × 16
#> source target weight_x weight_y raw_difference absolute_difference
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 adapt adapt 0 0 0 0
#> 2 cohesion adapt 0.00541 0 0.00541 0.00541
#> 3 consensus adapt 0.00435 0.00503 -0.000679 0.000679
#> 4 coregulate adapt 0.0252 0.0175 0.00769 0.00769
#> 5 discuss adapt 0.0103 0.140 -0.130 0.130
#> 6 emotion adapt 0.0101 0 0.0101 0.0101
#> 7 monitor adapt 0.00794 0.0127 -0.00480 0.00480
#> 8 plan adapt 0.00339 0 0.00339 0.00339
#> 9 synthesis adapt 0.175 0.333 -0.159 0.159
#> 10 adapt cohesion 0.2 0.291 -0.0907 0.0907
#> # ℹ 71 more rows
#> # ℹ 10 more variables: squared_difference <dbl>, relative_difference <dbl>,
#> # similarity_strength_index <dbl>, difference_index <dbl>,
#> # rank_difference <dbl>, percentile_difference <dbl>,
#> # logarithmic_ratio <dbl>, standardized_weight_x <dbl>,
#> # standardized_weight_y <dbl>, standardized_score_inflation <dbl>
#>
#> Summary metrics of differences
#> # A tibble: 24 × 3
#> category metric value
#> <chr> <chr> <dbl>
#> 1 Weight Deviations Mean Abs. Diff. 0.0437
#> 2 Weight Deviations Median Abs. Diff. 0.0278
#> 3 Weight Deviations RMS Diff. 0.0667
#> 4 Weight Deviations Max Abs. Diff. 0.251
#> 5 Weight Deviations Rel. Mean Abs. Diff. 0.393
#> 6 Weight Deviations CV Ratio 1.14
#> 7 Correlations Pearson 0.882
#> 8 Correlations Spearman 0.834
#> 9 Correlations Kendall 0.665
#> 10 Correlations Distance 0.772
#> # ℹ 14 more rows
#>
#> Network metrics
#> # A tibble: 13 × 3
#> metric x y
#> <chr> <dbl> <dbl>
#> 1 Node Count 9 e+ 0 9 e+ 0
#> 2 Edge Count 7.2 e+ 1 7.1 e+ 1
#> 3 Network Density 1 e+ 0 9.86e- 1
#> 4 Mean Distance 4.86e- 2 6.27e- 2
#> 5 Mean Out-Strength 1 e+ 0 1 e+ 0
#> 6 SD Out-Strength 9.68e- 1 7.04e- 1
#> 7 Mean In-Strength 1 e+ 0 1 e+ 0
#> 8 SD In-Strength 5.55e-17 3.93e-17
#> 9 Mean Out-Degree 8 e+ 0 7.89e+ 0
#> 10 SD Out-Degree 1.80e+ 0 1.17e+ 0
#> 11 Centralization (Out-Degree) 1.09e- 1 1.25e- 1
#> 12 Centralization (In-Degree) 1.09e- 1 1.25e- 1
#> 13 Reciprocity 8.92e- 1 8.75e- 1
#>
#> Centrality differences
#> # A tibble: 81 × 5
#> state centrality x y difference
#> <fct> <chr> <dbl> <dbl> <dbl>
#> 1 adapt OutStrength 1 1 0
#> 2 adapt InStrength 0.241 0.509 -0.267
#> 3 adapt ClosenessIn 11.4 3.85 7.52
#> 4 adapt ClosenessOut 1.11 1.55 -0.445
#> 5 adapt Closeness 15.4 6.32 9.13
#> 6 adapt Betweenness 0 4 -4
#> 7 adapt BetweennessRSP 1 3 -2
#> 8 adapt Diffusion 5.82 5.28 0.544
#> 9 adapt Clustering 0.406 0.285 0.121
#> 10 cohesion OutStrength 0.962 0.988 -0.0260
#> # ℹ 71 more rows
#>
#> Centrality correlations
#> # A tibble: 9 × 3
#> centrality Centrality correlation
#> <chr> <chr> <dbl>
#> 1 Betweenness Betweenness 0.324
#> 2 BetweennessRSP BetweennessRSP 0.986
#> 3 Closeness Closeness 0.501
#> 4 ClosenessIn ClosenessIn 0.606
#> 5 ClosenessOut ClosenessOut 0.330
#> 6 Clustering Clustering 0.831
#> 7 Diffusion Diffusion 0.980
#> 8 InStrength InStrength 0.971
#> 9 OutStrength OutStrength 0.983