Returns a summary of the occurrence count, proportion, and effect size for
each causal direction. Internally it calls get_causal_direction_counts(),
so that function's arguments can be passed through ....
Usage
# S3 method for class 'BootstrapResult'
tidy(x, ...)Arguments
- x
The return value of
lingam_direct_bootstrap()(aBootstrapResultobject)- ...
Arguments passed to
get_causal_direction_counts()(such asn_directions,min_causal_effect,split_by_causal_effect_sign,labels)
Examples
dat <- generate_lingam_sample_6()
bs <- lingam_direct_bootstrap(dat$data, n_sampling = 30L, reg_method = "ols", seed = 42)
#> Bootstrap: 30 iterations, method=ols (sequential)
#> iteration 1 / 30
#> iteration 10 / 30
#> iteration 20 / 30
#> iteration 30 / 30
#> Completed in 0.2 seconds.
tidy(bs)
#> from to count proportion mean_effect median_effect sd_effect ci_lower
#> 1 1 6 30 1.00000000 3.9169074323 4.003899696 0.21847967 3.445944525
#> 2 1 2 29 0.96666667 3.1965221742 3.068221838 0.30992203 2.841587361
#> 3 1 5 29 0.96666667 8.0389161985 8.006451065 0.10854439 7.880941612
#> 4 3 2 29 0.96666667 1.9673174150 1.966453543 0.05027476 1.879777124
#> 5 3 5 29 0.96666667 -1.0480749405 -1.053357169 0.06179135 -1.137049836
#> 6 4 1 29 0.96666667 3.1441078645 3.091442871 0.17659155 2.932002923
#> 7 4 2 29 0.96666667 0.0185014076 -0.008653801 0.22438674 -0.443300570
#> 8 4 3 29 0.96666667 6.0253099490 6.006990357 0.08010239 5.901857344
#> 9 4 5 29 0.96666667 0.4045350222 0.382649748 0.23513863 0.037881264
#> 10 4 6 29 0.96666667 -0.1622345285 -0.079885472 0.24550716 -0.646327934
#> 11 6 2 19 0.63333333 0.0153784233 0.008629175 0.03166615 -0.024662694
#> 12 5 2 18 0.60000000 -0.0500280758 -0.050117494 0.03396618 -0.097710792
#> 13 3 6 17 0.56666667 0.0857324665 0.085303740 0.07256644 -0.052388557
#> 14 6 5 17 0.56666667 -0.0006059292 -0.001975088 0.02392258 -0.041830447
#> 15 3 1 16 0.53333333 -0.0321416604 -0.035631751 0.03269372 -0.087898264
#> 16 1 3 14 0.46666667 -0.2478974798 -0.262812042 0.11310103 -0.438743808
#> 17 5 6 13 0.43333333 0.0349666609 0.029746764 0.03185837 -0.019204672
#> 18 6 3 13 0.43333333 0.0619274966 0.067423011 0.02340254 0.023069303
#> 19 2 5 12 0.40000000 0.0492728268 -0.047501331 0.27277363 -0.064307429
#> 20 2 6 11 0.36666667 -0.0128677846 -0.016891773 0.03405618 -0.061464005
#> 21 1 4 1 0.03333333 0.0111159044 0.011115904 0.00000000 0.011115904
#> 22 2 1 1 0.03333333 0.0428822972 0.042882297 0.00000000 0.042882297
#> 23 2 3 1 0.03333333 0.4080999577 0.408099958 0.00000000 0.408099958
#> 24 2 4 1 0.03333333 0.0010149466 0.001014947 0.00000000 0.001014947
#> 25 3 4 1 0.03333333 0.1360229125 0.136022912 0.00000000 0.136022912
#> 26 5 1 1 0.03333333 0.1075834029 0.107583403 0.00000000 0.107583403
#> 27 5 3 1 0.03333333 -0.1429104530 -0.142910453 0.00000000 -0.142910453
#> 28 5 4 1 0.03333333 0.0094097550 0.009409755 0.00000000 0.009409755
#> 29 6 4 1 0.03333333 -0.0049963432 -0.004996343 0.00000000 -0.004996343
#> ci_upper
#> 1 4.220378958
#> 2 3.779718648
#> 3 8.208283705
#> 4 2.047119882
#> 5 -0.932471742
#> 6 3.530778717
#> 7 0.357810619
#> 8 6.208911907
#> 9 0.902335801
#> 10 0.165966658
#> 11 0.079210779
#> 12 0.009318859
#> 13 0.190346233
#> 14 0.037238724
#> 15 0.023337357
#> 16 -0.066736406
#> 17 0.080052261
#> 18 0.094281792
#> 19 0.669829251
#> 20 0.051706273
#> 21 0.011115904
#> 22 0.042882297
#> 23 0.408099958
#> 24 0.001014947
#> 25 0.136022912
#> 26 0.107583403
#> 27 -0.142910453
#> 28 0.009409755
#> 29 -0.004996343