Convert a MultiGroupBootstrapResult to a tidy data.frame

Stacks each group's causal direction counts (via get_causal_direction_counts()) into a single data.frame with a group column in front. Arguments for get_causal_direction_counts() can be passed through ....

Usage

# S3 method for class 'MultiGroupBootstrapResult'
tidy(x, ...)

Arguments

x

The return value of lingam_multi_group_bootstrap() (a MultiGroupBootstrapResult object)

...

Arguments passed to get_causal_direction_counts() (such as n_directions, min_causal_effect, split_by_causal_effect_sign)

Value

data.frame (group, from, to, count, proportion, ...)

Examples

mg <- generate_multi_group_sample()
bs <- lingam_multi_group_bootstrap(mg$data_list,
  n_sampling = 10L, reg_method = "ols", seed = 42
)
#> Multi-group bootstrap: 10 iterations, 2 groups, method=ols (sequential)
#>   iteration 1 / 10
#>   iteration 10 / 10
#> Completed in 0.2 seconds.
tidy(bs)
#>     group from to count proportion   mean_effect median_effect   sd_effect
#> 1  group1    1  2    10        1.0  3.1505219787   3.114396602 0.340283649
#> 2  group1    1  5    10        1.0  8.0261281922   8.066656939 0.176106738
#> 3  group1    1  6    10        1.0  3.9930574378   4.008608422 0.037515635
#> 4  group1    3  2    10        1.0  1.9664336549   1.965744626 0.036222603
#> 5  group1    3  5    10        1.0 -1.0513640257  -1.052298383 0.043478046
#> 6  group1    4  1    10        1.0  3.1083461857   3.036353249 0.132949053
#> 7  group1    4  2    10        1.0  0.0148556541  -0.024931379 0.148470522
#> 8  group1    4  3    10        1.0  6.0663488172   6.044006814 0.089845045
#> 9  group1    4  5    10        1.0  0.3972825068   0.390033855 0.148458088
#> 10 group1    4  6    10        1.0 -0.1449685410  -0.128632536 0.242378840
#> 11 group1    6  2     8        0.8  0.0302721265   0.033136790 0.033305097
#> 12 group1    6  5     7        0.7 -0.0003181101  -0.001975088 0.039470630
#> 13 group1    3  6     6        0.6  0.0584168842   0.055517001 0.035405424
#> 14 group1    5  2     6        0.6 -0.0539595043  -0.056457097 0.023629916
#> 15 group1    1  3     6        0.6 -0.2051157480  -0.202358465 0.131121357
#> 16 group1    3  1     4        0.4 -0.0274602358  -0.026749845 0.020421414
#> 17 group1    6  3     4        0.4  0.0633217012   0.056588672 0.023223686
#> 18 group1    2  5     4        0.4 -0.0201782276  -0.038020240 0.043504482
#> 19 group1    5  6     3        0.3  0.0081391922   0.004471156 0.009806532
#> 20 group1    2  6     2        0.2 -0.0058791029  -0.005879103 0.020183628
#> 21 group2    1  2    10        1.0  2.8600523622   2.796090928 0.354313682
#> 22 group2    1  5    10        1.0  8.4653642370   8.479257163 0.117505104
#> 23 group2    1  6    10        1.0  4.3855518426   4.503698478 0.221940292
#> 24 group2    3  2    10        1.0  2.5387516152   2.530543746 0.030915116
#> 25 group2    3  5    10        1.0 -1.5236676739  -1.503166377 0.073662876
#> 26 group2    4  1    10        1.0  3.4390851754   3.504709732 0.142449133
#> 27 group2    4  2    10        1.0  0.1939327666   0.204660427 0.196409840
#> 28 group2    4  3    10        1.0  6.4084765947   6.464724033 0.125898129
#> 29 group2    4  5    10        1.0 -0.0285916036  -0.025592644 0.163468232
#> 30 group2    4  6    10        1.0  0.1114682499   0.090186346 0.186764353
#> 31 group2    6  2     8        0.8  0.1116112427   0.116138921 0.034410894
#> 32 group2    6  5     7        0.7  0.0003010369   0.002520063 0.019485038
#> 33 group2    3  6     6        0.6 -0.1175436162  -0.031303583 0.180728714
#> 34 group2    5  2     6        0.6  0.0312784151   0.030562171 0.025031126
#> 35 group2    1  3     6        0.6  0.0861857287   0.085285926 0.055205966
#> 36 group2    3  1     4        0.4  0.0243812440   0.031356251 0.028956968
#> 37 group2    6  3     4        0.4 -0.0161420138  -0.017139865 0.014526373
#> 38 group2    2  5     4        0.4  0.0269502598   0.022694194 0.027627485
#> 39 group2    5  6     3        0.3  0.0186342077   0.009623112 0.017921234
#> 40 group2    2  6     2        0.2  0.1100739608   0.110073961 0.076891845
#>         ci_lower     ci_upper
#> 1   2.6965000284  3.581868037
#> 2   7.7122751699  8.201090164
#> 3   3.9224410563  4.028152005
#> 4   1.9189491703  2.021619157
#> 5  -1.1242614737 -0.990869377
#> 6   2.9867918834  3.357506668
#> 7  -0.1455434816  0.254745398
#> 8   5.9598997782  6.215738098
#> 9   0.1844745349  0.638681411
#> 10 -0.5298535704  0.175646430
#> 11 -0.0203936639  0.065877847
#> 12 -0.0440660032  0.064352173
#> 13  0.0118763392  0.105918848
#> 14 -0.0788445229 -0.022677911
#> 15 -0.3913836308 -0.062677078
#> 16 -0.0512459358 -0.004882201
#> 17  0.0452678219  0.092821730
#> 18 -0.0483416081  0.038316575
#> 19  0.0008842163  0.018511998
#> 20 -0.0194374840  0.007679278
#> 21  2.4256480128  3.402808918
#> 22  8.2979286610  8.651689362
#> 23  3.9589564925  4.549251325
#> 24  2.5024225141  2.591016030
#> 25 -1.6704019399 -1.460874001
#> 26  3.1957191948  3.576983271
#> 27 -0.0339727651  0.540926440
#> 28  6.1879889831  6.523271879
#> 29 -0.2715221132  0.213226500
#> 30 -0.1300180221  0.395852794
#> 31  0.0639456216  0.159850941
#> 32 -0.0263003339  0.029793800
#> 33 -0.4238513761  0.013011064
#> 34  0.0044643042  0.057821787
#> 35  0.0066241460  0.143211994
#> 36 -0.0115258356  0.048430812
#> 37 -0.0317006402  0.001112960
#> 38  0.0021005018  0.059035330
#> 39  0.0071376606  0.037790186
#> 40  0.0584217529  0.161726169