Compare Grouped TNA Models with Comprehensive Metrics

Compare Grouped TNA Models with Comprehensive Metrics

Usage

# S3 method for class 'group_tna'
compare(
  x,
  i = 1L,
  j = 2L,
  scaling = "none",
  measures = character(0),
  network = TRUE,
  ...
)

Arguments

x

A group_tna object.

i

An integer index or the name of the principal cluster as a character string.

j

An integer index or the name of the secondary cluster as a character string.

scaling

A character string naming a scaling method to apply to the weights before comparing them. The supported options are:

• "none": No scaling is performed. The weights are used as is.

• "minmax": Performs min-max normalization, i.e., the minimum value is subtracted and the differences are scaled by the range.

• "max": Max-normalization: the values are divided by the maximum value.

• "rank": Applies min-max normalization to the ranks of the weights (computed with ties.method = "average").

• "zscore": Computes the standard score, i.e. the mean weight is subtracted and the differences are scaled by the standard deviation.

• "robust": Computes the robust z-score, i.e. the median weight is subtracted and the differences are scaled by the median absolute deviation (using stats::mad).

• "log": Simply the natural logarithm of the weights.

• "log1p": As above, but adds 1 to the values before taking the logarithm. Useful for scenarios with zero weights.

• "softmax": Performs softmax normalization.

• "quantile": Uses the empirical quantiles of the weights via stats::ecdf.

measures

A character vector indicating which centrality measures should be computed. See centralities() for the available measures. No measures are included by default.

network

A logical value indicating whether network metrics should be included in the comparison. The default is TRUE.

...

Additional arguments passed to compare.tna().

Value

A tna_comparison object. See compare.tna() for details.

See also

Model comparison functions compare(), compare_sequences(), plot.tna_comparison(), plot.tna_sequence_comparison(), plot_compare(), plot_compare.group_tna(), print.tna_comparison(), print.tna_sequence_comparison()

Examples

model <- group_model(engagement_mmm)
compare(model, i = 1, j = 2)
#> Edge difference metrics
#> # A tibble: 9 × 16
#>   source     target     weight_x weight_y raw_difference absolute_difference
#>   <fct>      <fct>         <dbl>    <dbl>          <dbl>               <dbl>
#> 1 Active     Active       0.860    0.841          0.0189              0.0189
#> 2 Average    Active       0.312    0.0926         0.220               0.220 
#> 3 Disengaged Active       0.0479   0.156         -0.108               0.108 
#> 4 Active     Average      0.0892   0.159         -0.0699              0.0699
#> 5 Average    Average      0.542    0.630         -0.0875              0.0875
#> 6 Disengaged Average      0.162    0.511         -0.349               0.349 
#> 7 Active     Disengaged   0.0509   0              0.0509              0.0509
#> 8 Average    Disengaged   0.146    0.278         -0.132               0.132 
#> 9 Disengaged Disengaged   0.790    0.333          0.457               0.457 
#> # 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: 22 × 3
#>    category          metric               value
#>    <chr>             <chr>                <dbl>
#>  1 Weight Deviations Mean Abs. Diff.      0.166
#>  2 Weight Deviations Median Abs. Diff.    0.108
#>  3 Weight Deviations RMS Diff.            0.217
#>  4 Weight Deviations Max Abs. Diff.       0.457
#>  5 Weight Deviations Rel. Mean Abs. Diff. 0.498
#>  6 Weight Deviations CV Ratio             1.16 
#>  7 Correlations      Pearson              0.710
#>  8 Correlations      Spearman             0.733
#>  9 Correlations      Kendall              0.611
#> 10 Correlations      Distance             0.500
#> # ℹ 12 more rows
#> 
#> Network metrics
#> # A tibble: 13 × 3
#>    metric                          x     y
#>    <chr>                       <dbl> <dbl>
#>  1 Node Count                  3     3    
#>  2 Edge Count                  9     8    
#>  3 Network Density             1     1    
#>  4 Mean Distance               0.111 0.239
#>  5 Mean Out-Strength           1     1    
#>  6 SD Out-Strength             0.214 0.353
#>  7 Mean In-Strength            1     1    
#>  8 SD In-Strength              0     0    
#>  9 Mean Out-Degree             3     2.67 
#> 10 SD Out-Degree               0     0.577
#> 11 Centralization (Out-Degree) 0     0.25 
#> 12 Centralization (In-Degree)  0     0.25 
#> 13 Reciprocity                 1     0.8