Print Permutation Test Results
Usage
# S3 method for class 'tna_permutation'
print(x, ...)See also
Validation functions
bootstrap(),
deprune(),
estimate_cs(),
permutation_test(),
permutation_test.group_tna(),
plot.group_tna_bootstrap(),
plot.group_tna_permutation(),
plot.group_tna_stability(),
plot.tna_bootstrap(),
plot.tna_permutation(),
plot.tna_stability(),
print.group_tna_bootstrap(),
print.group_tna_permutation(),
print.group_tna_stability(),
print.summary.group_tna_bootstrap(),
print.summary.tna_bootstrap(),
print.tna_bootstrap(),
print.tna_stability(),
prune(),
pruning_details(),
reprune(),
summary.group_tna_bootstrap(),
summary.tna_bootstrap()
Examples
model_x <- tna(group_regulation[1:200, ])
model_y <- tna(group_regulation[1001:1200, ])
# Small number of iterations for CRAN
perm <- permutation_test(model_x, model_y, iter = 20)
print(perm)
#> # A tibble: 81 × 4
#> edge_name diff_true effect_size p_value
#> <chr> <dbl> <dbl> <dbl>
#> 1 adapt -> adapt 0 NaN 1
#> 2 cohesion -> adapt 0.00541 0.925 0.857
#> 3 consensus -> adapt -0.000679 -0.173 0.810
#> 4 coregulate -> adapt 0.00769 0.494 0.667
#> 5 discuss -> adapt -0.130 -7.09 0.0476
#> 6 emotion -> adapt 0.0101 1.64 0.286
#> 7 monitor -> adapt -0.00480 -0.444 0.952
#> 8 plan -> adapt 0.00339 1.25 0.0476
#> 9 synthesis -> adapt -0.159 -1.78 0.143
#> 10 adapt -> cohesion -0.0907 -1.02 0.476
#> # ℹ 71 more rows