Convert an ImputationBootstrapResult to a tidy data.frame
Source:R/tidiers.r
tidy.ImputationBootstrapResult.RdCollapses the imputation dimension with as_bootstrap_result() and then
summarizes the causal direction counts like tidy.BootstrapResult().
Arguments
- x
The return value of
bootstrap_with_imputation()(anImputationBootstrapResultobject)- aggregate
How to collapse the
n_repeatsimputation dimension, passed toas_bootstrap_result()("median" or "mean")- ...
Arguments passed to
get_causal_direction_counts()
Examples
dat <- generate_lingam_sample_6(n = 200, seed = 1)$data
dat[sample(nrow(dat), 20), 1] <- NA
bs <- bootstrap_with_imputation(dat, n_sampling = 5L, n_repeats = 2L, seed = 42)
#> Bootstrap with imputation: 5 iterations, n_repeats=2 (sequential)
#> iteration 1 / 5
#> Completed in 0.2 seconds.
tidy(bs)
#> from to count proportion mean_effect median_effect sd_effect ci_lower
#> 1 3 2 5 1.0 2.1383915 2.0617327 0.176654962 1.9992568
#> 2 4 3 5 1.0 5.9970268 5.9873049 0.057209867 5.9427038
#> 3 4 5 5 1.0 18.7973718 18.1917914 1.926593886 17.4957668
#> 4 5 1 5 1.0 0.1552270 0.1565516 0.004672225 0.1479719
#> 5 1 2 4 0.8 2.6377799 2.8992287 0.564333100 1.8736513
#> 6 3 6 4 0.8 0.4542938 0.5195260 0.152648364 0.2481890
#> 7 5 6 4 0.8 0.4893890 0.4897081 0.006038851 0.4832631
#> 8 5 2 2 0.4 0.2463576 0.2463576 0.158645772 0.1397871
#> 9 3 5 1 0.2 -0.7811145 -0.7811145 0.000000000 -0.7811145
#> 10 1 6 1 0.2 3.9956585 3.9956585 0.000000000 3.9956585
#> 11 4 6 1 0.2 1.9828259 1.9828259 0.000000000 1.9828259
#> ci_upper
#> 1 2.3998647
#> 2 6.0791722
#> 3 21.8022401
#> 4 0.1585205
#> 5 2.9574454
#> 6 0.5495037
#> 7 0.4949723
#> 8 0.3529282
#> 9 -0.7811145
#> 10 3.9956585
#> 11 1.9828259