Summarize Bootstrap Results
Usage
# S3 method for class 'tna_bootstrap'
summary(object, ...)Value
A summary.tna_bootstrap object containing the weight,
estimated p-value and confidence interval of each edge.
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_reliability(),
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_clustering(),
print.tna_permutation(),
print.tna_reliability(),
print.tna_stability(),
prune(),
pruning_details(),
reliability(),
reprune(),
summary.group_tna_bootstrap()
Examples
model <- tna(group_regulation)
# Small number of iterations for CRAN
boot <- bootstrap(model, iter = 50)
summary(boot)
#> from to weight p_value sig cr_lower cr_upper
#> 2 cohesion adapt 0.0029498525 0.43137255 FALSE 0.0022123894 0.003687316
#> 3 consensus adapt 0.0047400853 0.17647059 FALSE 0.0035550640 0.005925107
#> 4 coregulate adapt 0.0162436548 0.31372549 FALSE 0.0121827411 0.020304569
#> 5 discuss adapt 0.0713743356 0.01960784 TRUE 0.0535307517 0.089217920
#> 6 emotion adapt 0.0024673951 0.62745098 FALSE 0.0018505464 0.003084244
#> 7 monitor adapt 0.0111653873 0.25490196 FALSE 0.0083740405 0.013956734
#> 8 plan adapt 0.0009745006 0.58823529 FALSE 0.0007308754 0.001218126
#> 9 synthesis adapt 0.2346625767 0.01960784 TRUE 0.1759969325 0.293328221
#> 10 adapt cohesion 0.2730844794 0.01960784 TRUE 0.2048133595 0.341355599
#> 11 cohesion cohesion 0.0271386431 0.11764706 FALSE 0.0203539823 0.033923304
#> 12 consensus cohesion 0.0148522673 0.01960784 TRUE 0.0111392005 0.018565334
#> 13 coregulate cohesion 0.0360406091 0.07843137 FALSE 0.0270304569 0.045050761
#> 14 discuss cohesion 0.0475828904 0.01960784 TRUE 0.0356871678 0.059478613
#> 15 emotion cohesion 0.3253436729 0.01960784 TRUE 0.2440077547 0.406679591
#> 16 monitor cohesion 0.0558269365 0.01960784 TRUE 0.0418702024 0.069783671
#> 17 plan cohesion 0.0251745980 0.01960784 TRUE 0.0188809485 0.031468248
#> 18 synthesis cohesion 0.0337423313 0.27450980 FALSE 0.0253067485 0.042177914
#> 19 adapt consensus 0.4774066798 0.01960784 TRUE 0.3580550098 0.596758350
#> 20 cohesion consensus 0.4979351032 0.01960784 TRUE 0.3734513274 0.622418879
#> 21 consensus consensus 0.0820034761 0.01960784 TRUE 0.0615026070 0.102504345
#> 22 coregulate consensus 0.1345177665 0.01960784 TRUE 0.1008883249 0.168147208
#> 23 discuss consensus 0.3211845103 0.01960784 TRUE 0.2408883827 0.401480638
#> 24 emotion consensus 0.3204088826 0.01960784 TRUE 0.2403066620 0.400511103
#> 25 monitor consensus 0.1591067690 0.01960784 TRUE 0.1193300768 0.198883461
#> 26 plan consensus 0.2904011694 0.01960784 TRUE 0.2178008771 0.363001462
#> 27 synthesis consensus 0.4662576687 0.01960784 TRUE 0.3496932515 0.582822086
#> 28 adapt coregulate 0.0216110020 0.37254902 FALSE 0.0162082515 0.027013752
#> 29 cohesion coregulate 0.1191740413 0.01960784 TRUE 0.0893805310 0.148967552
#> 30 consensus coregulate 0.1877073787 0.01960784 TRUE 0.1407805340 0.234634223
#> 31 coregulate coregulate 0.0233502538 0.07843137 FALSE 0.0175126904 0.029187817
#> 32 discuss coregulate 0.0842824601 0.01960784 TRUE 0.0632118451 0.105353075
#> 33 emotion coregulate 0.0341910469 0.05882353 FALSE 0.0256432852 0.042738809
#> 34 monitor coregulate 0.0579204466 0.01960784 TRUE 0.0434403350 0.072400558
#> 35 plan coregulate 0.0172161767 0.01960784 TRUE 0.0129121325 0.021520221
#> 36 synthesis coregulate 0.0444785276 0.13725490 FALSE 0.0333588957 0.055598160
#> 37 adapt discuss 0.0589390963 0.17647059 FALSE 0.0442043222 0.073673870
#> 38 cohesion discuss 0.0595870206 0.01960784 TRUE 0.0446902655 0.074483776
#> 39 consensus discuss 0.1880233844 0.01960784 TRUE 0.1410175383 0.235029231
#> 40 coregulate discuss 0.2736040609 0.01960784 TRUE 0.2052030457 0.342005076
#> 41 discuss discuss 0.1948873703 0.01960784 TRUE 0.1461655277 0.243609213
#> 42 emotion discuss 0.1018681706 0.01960784 TRUE 0.0764011280 0.127335213
#> 43 monitor discuss 0.3754361479 0.01960784 TRUE 0.2815771110 0.469295185
#> 44 plan discuss 0.0678902063 0.01960784 TRUE 0.0509176547 0.084862758
#> 45 synthesis discuss 0.0628834356 0.21568627 FALSE 0.0471625767 0.078604294
#> 46 adapt emotion 0.1198428291 0.05882353 FALSE 0.0898821218 0.149803536
#> 47 cohesion emotion 0.1156342183 0.01960784 TRUE 0.0867256637 0.144542773
#> 48 consensus emotion 0.0726813083 0.01960784 TRUE 0.0545109812 0.090851635
#> 49 coregulate emotion 0.1720812183 0.01960784 TRUE 0.1290609137 0.215101523
#> 50 discuss emotion 0.1057960010 0.01960784 TRUE 0.0793470008 0.132245001
#> 51 emotion emotion 0.0768417342 0.01960784 TRUE 0.0576313007 0.096052168
#> 52 monitor emotion 0.0907187718 0.01960784 TRUE 0.0680390789 0.113398465
#> 53 plan emotion 0.1468247523 0.01960784 TRUE 0.1101185642 0.183530940
#> 54 synthesis emotion 0.0705521472 0.11764706 FALSE 0.0529141104 0.088190184
#> 55 adapt monitor 0.0333988212 0.33333333 FALSE 0.0250491159 0.041748527
#> 56 cohesion monitor 0.0330383481 0.05882353 FALSE 0.0247787611 0.041297935
#> 57 consensus monitor 0.0466108390 0.01960784 TRUE 0.0349581292 0.058263549
#> 58 coregulate monitor 0.0862944162 0.01960784 TRUE 0.0647208122 0.107868020
#> 59 discuss monitor 0.0222728423 0.01960784 TRUE 0.0167046317 0.027841053
#> 60 emotion monitor 0.0363059570 0.03921569 TRUE 0.0272294677 0.045382446
#> 61 monitor monitor 0.0181437544 0.17647059 FALSE 0.0136078158 0.022679693
#> 62 plan monitor 0.0755237941 0.01960784 TRUE 0.0566428455 0.094404743
#> 63 synthesis monitor 0.0122699387 0.49019608 FALSE 0.0092024540 0.015337423
#> 64 adapt plan 0.0157170923 0.49019608 FALSE 0.0117878193 0.019646365
#> 65 cohesion plan 0.1410029499 0.01960784 TRUE 0.1057522124 0.176253687
#> 66 consensus plan 0.3957971243 0.01960784 TRUE 0.2968478433 0.494746405
#> 67 coregulate plan 0.2390862944 0.01960784 TRUE 0.1793147208 0.298857868
#> 68 discuss plan 0.0116426221 0.07843137 FALSE 0.0087319666 0.014553278
#> 69 emotion plan 0.0997532605 0.01960784 TRUE 0.0748149454 0.124691576
#> 70 monitor plan 0.2156315422 0.01960784 TRUE 0.1617236567 0.269539428
#> 71 plan plan 0.3742082183 0.01960784 TRUE 0.2806561637 0.467760273
#> 72 synthesis plan 0.0751533742 0.11764706 FALSE 0.0563650307 0.093941718
#> 74 cohesion synthesis 0.0035398230 0.49019608 FALSE 0.0026548673 0.004424779
#> 75 consensus synthesis 0.0075841365 0.11764706 FALSE 0.0056881024 0.009480171
#> 76 coregulate synthesis 0.0187817259 0.05882353 FALSE 0.0140862944 0.023477157
#> 77 discuss synthesis 0.1409769679 0.01960784 TRUE 0.1057327259 0.176221210
#> 78 emotion synthesis 0.0028198802 0.50980392 FALSE 0.0021149101 0.003524850
#> 79 monitor synthesis 0.0160502442 0.23529412 FALSE 0.0120376832 0.020062805
#> 80 plan synthesis 0.0017865844 0.31372549 FALSE 0.0013399383 0.002233230
#> ci_lower ci_upper
#> 2 0.0007139782 0.005083812
#> 3 0.0029410969 0.006247046
#> 4 0.0104347075 0.023217566
#> 5 0.0648256558 0.078733278
#> 6 0.0010220446 0.004407829
#> 7 0.0070408825 0.016153450
#> 8 0.0002002347 0.001896876
#> 9 0.2130179730 0.265176155
#> 10 0.2441255124 0.304786383
#> 11 0.0191064225 0.034149468
#> 12 0.0119006121 0.017122454
#> 13 0.0291317912 0.045644493
#> 14 0.0424461826 0.052789277
#> 15 0.3087007097 0.338183150
#> 16 0.0472331441 0.066897472
#> 17 0.0219155791 0.027941697
#> 18 0.0227218862 0.046615362
#> 19 0.4344606164 0.516231334
#> 20 0.4764911219 0.519428619
#> 21 0.0752744538 0.089387326
#> 22 0.1212027625 0.146906660
#> 23 0.3107788856 0.334535867
#> 24 0.3035417223 0.336703391
#> 25 0.1341907922 0.175522286
#> 26 0.2811735996 0.300259895
#> 27 0.4335368863 0.497724444
#> 28 0.0124403611 0.037478205
#> 29 0.1041468334 0.130656663
#> 30 0.1766412137 0.196081779
#> 31 0.0176999642 0.030004792
#> 32 0.0790853288 0.090874861
#> 33 0.0283519686 0.042418889
#> 34 0.0493636797 0.069395531
#> 35 0.0148299723 0.020117332
#> 36 0.0308187790 0.059347287
#> 37 0.0415888141 0.077174219
#> 38 0.0520036166 0.070888957
#> 39 0.1744552301 0.198336064
#> 40 0.2568423351 0.291171559
#> 41 0.1841933737 0.202221037
#> 42 0.0953882929 0.112077502
#> 43 0.3470871283 0.398450681
#> 44 0.0633580324 0.072491649
#> 45 0.0426057904 0.080943843
#> 46 0.0948213271 0.146662004
#> 47 0.1017478219 0.128793087
#> 48 0.0671624568 0.078295224
#> 49 0.1553743770 0.189757767
#> 50 0.0961830927 0.116506689
#> 51 0.0652956315 0.087930911
#> 52 0.0790167349 0.104550080
#> 53 0.1420563944 0.154483953
#> 54 0.0530603192 0.093010789
#> 55 0.0172084130 0.057412171
#> 56 0.0255810679 0.041006298
#> 57 0.0413167211 0.052229529
#> 58 0.0759965921 0.097142290
#> 59 0.0176087636 0.025904893
#> 60 0.0307235644 0.041656714
#> 61 0.0121178052 0.024149410
#> 62 0.0695488561 0.084261641
#> 63 0.0065081785 0.020891375
#> 64 0.0094753295 0.025468515
#> 65 0.1288738682 0.154954906
#> 66 0.3860149888 0.407607862
#> 67 0.2199015224 0.255678801
#> 68 0.0090057909 0.014906042
#> 69 0.0887715594 0.112766952
#> 70 0.1987974267 0.237037860
#> 71 0.3588655923 0.386317646
#> 72 0.0538087926 0.096069657
#> 74 0.0012923853 0.005782532
#> 75 0.0051756031 0.009363860
#> 76 0.0132043988 0.022831886
#> 77 0.1283215535 0.147817060
#> 78 0.0010430908 0.004349185
#> 79 0.0093518028 0.023603494
#> 80 0.0009793548 0.002443987