Print a Bootstrap Summary
Usage
# S3 method for class 'summary.tna_bootstrap'
print(x, ...)Value
A summary.tna_bootstrap object (invisibly) 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_stability(),
print.group_tna_bootstrap(),
print.group_tna_permutation(),
print.group_tna_stability(),
print.summary.group_tna_bootstrap(),
print.tna_bootstrap(),
print.tna_permutation(),
print.tna_stability(),
prune(),
pruning_details(),
reprune(),
summary.group_tna_bootstrap(),
summary.tna_bootstrap()
Examples
model <- tna(group_regulation)
# Small number of iterations for CRAN
boot <- bootstrap(model, iter = 10)
print(summary(boot))
#> from to weight p_value sig cr_lower cr_upper
#> 2 cohesion adapt 0.0029498525 0.72727273 FALSE 0.0022123894 0.003687316
#> 3 consensus adapt 0.0047400853 0.09090909 FALSE 0.0035550640 0.005925107
#> 4 coregulate adapt 0.0162436548 0.18181818 FALSE 0.0121827411 0.020304569
#> 5 discuss adapt 0.0713743356 0.09090909 FALSE 0.0535307517 0.089217920
#> 6 emotion adapt 0.0024673951 0.63636364 FALSE 0.0018505464 0.003084244
#> 7 monitor adapt 0.0111653873 0.36363636 FALSE 0.0083740405 0.013956734
#> 8 plan adapt 0.0009745006 0.63636364 FALSE 0.0007308754 0.001218126
#> 9 synthesis adapt 0.2346625767 0.09090909 FALSE 0.1759969325 0.293328221
#> 10 adapt cohesion 0.2730844794 0.09090909 FALSE 0.2048133595 0.341355599
#> 11 cohesion cohesion 0.0271386431 0.18181818 FALSE 0.0203539823 0.033923304
#> 12 consensus cohesion 0.0148522673 0.09090909 FALSE 0.0111392005 0.018565334
#> 13 coregulate cohesion 0.0360406091 0.18181818 FALSE 0.0270304569 0.045050761
#> 14 discuss cohesion 0.0475828904 0.18181818 FALSE 0.0356871678 0.059478613
#> 15 emotion cohesion 0.3253436729 0.09090909 FALSE 0.2440077547 0.406679591
#> 16 monitor cohesion 0.0558269365 0.18181818 FALSE 0.0418702024 0.069783671
#> 17 plan cohesion 0.0251745980 0.09090909 FALSE 0.0188809485 0.031468248
#> 18 synthesis cohesion 0.0337423313 0.27272727 FALSE 0.0253067485 0.042177914
#> 19 adapt consensus 0.4774066798 0.09090909 FALSE 0.3580550098 0.596758350
#> 20 cohesion consensus 0.4979351032 0.09090909 FALSE 0.3734513274 0.622418879
#> 21 consensus consensus 0.0820034761 0.09090909 FALSE 0.0615026070 0.102504345
#> 22 coregulate consensus 0.1345177665 0.09090909 FALSE 0.1008883249 0.168147208
#> 23 discuss consensus 0.3211845103 0.09090909 FALSE 0.2408883827 0.401480638
#> 24 emotion consensus 0.3204088826 0.09090909 FALSE 0.2403066620 0.400511103
#> 25 monitor consensus 0.1591067690 0.09090909 FALSE 0.1193300768 0.198883461
#> 26 plan consensus 0.2904011694 0.09090909 FALSE 0.2178008771 0.363001462
#> 27 synthesis consensus 0.4662576687 0.09090909 FALSE 0.3496932515 0.582822086
#> 28 adapt coregulate 0.0216110020 0.27272727 FALSE 0.0162082515 0.027013752
#> 29 cohesion coregulate 0.1191740413 0.09090909 FALSE 0.0893805310 0.148967552
#> 30 consensus coregulate 0.1877073787 0.09090909 FALSE 0.1407805340 0.234634223
#> 31 coregulate coregulate 0.0233502538 0.09090909 FALSE 0.0175126904 0.029187817
#> 32 discuss coregulate 0.0842824601 0.09090909 FALSE 0.0632118451 0.105353075
#> 33 emotion coregulate 0.0341910469 0.09090909 FALSE 0.0256432852 0.042738809
#> 34 monitor coregulate 0.0579204466 0.09090909 FALSE 0.0434403350 0.072400558
#> 35 plan coregulate 0.0172161767 0.09090909 FALSE 0.0129121325 0.021520221
#> 36 synthesis coregulate 0.0444785276 0.18181818 FALSE 0.0333588957 0.055598160
#> 37 adapt discuss 0.0589390963 0.27272727 FALSE 0.0442043222 0.073673870
#> 38 cohesion discuss 0.0595870206 0.09090909 FALSE 0.0446902655 0.074483776
#> 39 consensus discuss 0.1880233844 0.09090909 FALSE 0.1410175383 0.235029231
#> 40 coregulate discuss 0.2736040609 0.09090909 FALSE 0.2052030457 0.342005076
#> 41 discuss discuss 0.1948873703 0.09090909 FALSE 0.1461655277 0.243609213
#> 42 emotion discuss 0.1018681706 0.09090909 FALSE 0.0764011280 0.127335213
#> 43 monitor discuss 0.3754361479 0.09090909 FALSE 0.2815771110 0.469295185
#> 44 plan discuss 0.0678902063 0.09090909 FALSE 0.0509176547 0.084862758
#> 45 synthesis discuss 0.0628834356 0.09090909 FALSE 0.0471625767 0.078604294
#> 46 adapt emotion 0.1198428291 0.09090909 FALSE 0.0898821218 0.149803536
#> 47 cohesion emotion 0.1156342183 0.09090909 FALSE 0.0867256637 0.144542773
#> 48 consensus emotion 0.0726813083 0.09090909 FALSE 0.0545109812 0.090851635
#> 49 coregulate emotion 0.1720812183 0.09090909 FALSE 0.1290609137 0.215101523
#> 50 discuss emotion 0.1057960010 0.09090909 FALSE 0.0793470008 0.132245001
#> 51 emotion emotion 0.0768417342 0.09090909 FALSE 0.0576313007 0.096052168
#> 52 monitor emotion 0.0907187718 0.09090909 FALSE 0.0680390789 0.113398465
#> 53 plan emotion 0.1468247523 0.09090909 FALSE 0.1101185642 0.183530940
#> 54 synthesis emotion 0.0705521472 0.09090909 FALSE 0.0529141104 0.088190184
#> 55 adapt monitor 0.0333988212 0.27272727 FALSE 0.0250491159 0.041748527
#> 56 cohesion monitor 0.0330383481 0.18181818 FALSE 0.0247787611 0.041297935
#> 57 consensus monitor 0.0466108390 0.09090909 FALSE 0.0349581292 0.058263549
#> 58 coregulate monitor 0.0862944162 0.09090909 FALSE 0.0647208122 0.107868020
#> 59 discuss monitor 0.0222728423 0.09090909 FALSE 0.0167046317 0.027841053
#> 60 emotion monitor 0.0363059570 0.09090909 FALSE 0.0272294677 0.045382446
#> 61 monitor monitor 0.0181437544 0.27272727 FALSE 0.0136078158 0.022679693
#> 62 plan monitor 0.0755237941 0.09090909 FALSE 0.0566428455 0.094404743
#> 63 synthesis monitor 0.0122699387 0.54545455 FALSE 0.0092024540 0.015337423
#> 64 adapt plan 0.0157170923 0.72727273 FALSE 0.0117878193 0.019646365
#> 65 cohesion plan 0.1410029499 0.09090909 FALSE 0.1057522124 0.176253687
#> 66 consensus plan 0.3957971243 0.09090909 FALSE 0.2968478433 0.494746405
#> 67 coregulate plan 0.2390862944 0.09090909 FALSE 0.1793147208 0.298857868
#> 68 discuss plan 0.0116426221 0.09090909 FALSE 0.0087319666 0.014553278
#> 69 emotion plan 0.0997532605 0.09090909 FALSE 0.0748149454 0.124691576
#> 70 monitor plan 0.2156315422 0.09090909 FALSE 0.1617236567 0.269539428
#> 71 plan plan 0.3742082183 0.09090909 FALSE 0.2806561637 0.467760273
#> 72 synthesis plan 0.0751533742 0.09090909 FALSE 0.0563650307 0.093941718
#> 74 cohesion synthesis 0.0035398230 0.45454545 FALSE 0.0026548673 0.004424779
#> 75 consensus synthesis 0.0075841365 0.18181818 FALSE 0.0056881024 0.009480171
#> 76 coregulate synthesis 0.0187817259 0.18181818 FALSE 0.0140862944 0.023477157
#> 77 discuss synthesis 0.1409769679 0.09090909 FALSE 0.1057327259 0.176221210
#> 78 emotion synthesis 0.0028198802 0.45454545 FALSE 0.0021149101 0.003524850
#> 79 monitor synthesis 0.0160502442 0.36363636 FALSE 0.0120376832 0.020062805
#> 80 plan synthesis 0.0017865844 0.54545455 FALSE 0.0013399383 0.002233230
#> ci_lower ci_upper
#> 2 0.0017699141 0.005169433
#> 3 0.0039519825 0.005478068
#> 4 0.0112647400 0.019355496
#> 5 0.0672875413 0.078280610
#> 6 0.0005157291 0.003081815
#> 7 0.0077723479 0.014095383
#> 8 0.0003584611 0.001543080
#> 9 0.2056540136 0.250346154
#> 10 0.2335636176 0.296875253
#> 11 0.0187949640 0.032296493
#> 12 0.0127314251 0.016650829
#> 13 0.0283950896 0.043028584
#> 14 0.0369084902 0.052026448
#> 15 0.3150776038 0.349715469
#> 16 0.0420358512 0.066692976
#> 17 0.0236096713 0.029607325
#> 18 0.0276464804 0.050095151
#> 19 0.4458011399 0.535285021
#> 20 0.4743713867 0.507285970
#> 21 0.0758980410 0.084672616
#> 22 0.1230441788 0.144846294
#> 23 0.3112982315 0.334708966
#> 24 0.3083387698 0.334923564
#> 25 0.1491178147 0.166173544
#> 26 0.2831459308 0.299795782
#> 27 0.4415772929 0.483338576
#> 28 0.0148742289 0.026040442
#> 29 0.1092779171 0.131766682
#> 30 0.1827223585 0.190998605
#> 31 0.0182673334 0.025002824
#> 32 0.0798211621 0.091395419
#> 33 0.0275411086 0.037515929
#> 34 0.0521977788 0.067221151
#> 35 0.0154996063 0.018732351
#> 36 0.0339455035 0.057330505
#> 37 0.0410997283 0.076700404
#> 38 0.0513828954 0.068547232
#> 39 0.1838605936 0.195616030
#> 40 0.2663362206 0.286236811
#> 41 0.1841512121 0.203676169
#> 42 0.0905210058 0.105124037
#> 43 0.3561589888 0.389964471
#> 44 0.0646519124 0.076655687
#> 45 0.0559423648 0.076670778
#> 46 0.1084131081 0.134564344
#> 47 0.1089941228 0.127386056
#> 48 0.0666499018 0.075587454
#> 49 0.1623140420 0.178963216
#> 50 0.0978638425 0.114642372
#> 51 0.0662853732 0.083143133
#> 52 0.0822515167 0.099184369
#> 53 0.1352410368 0.149826590
#> 54 0.0550237726 0.078999503
#> 55 0.0236615618 0.042323238
#> 56 0.0244838654 0.038437716
#> 57 0.0415016903 0.050551498
#> 58 0.0731026415 0.088367495
#> 59 0.0202502349 0.026067603
#> 60 0.0297772915 0.039379589
#> 61 0.0124388821 0.024827156
#> 62 0.0717071288 0.080061147
#> 63 0.0064698175 0.019428066
#> 64 0.0098824618 0.024629106
#> 65 0.1320521920 0.152941110
#> 66 0.3910146588 0.407072943
#> 67 0.2330088982 0.250077612
#> 68 0.0094166970 0.014144265
#> 69 0.0946868664 0.110773878
#> 70 0.1971199402 0.232264463
#> 71 0.3685676942 0.380364890
#> 72 0.0614811340 0.083968716
#> 74 0.0007244854 0.005083641
#> 75 0.0065941071 0.009354952
#> 76 0.0148728031 0.023901758
#> 77 0.1327579225 0.152810105
#> 78 0.0013078861 0.004478534
#> 79 0.0100696966 0.023008198
#> 80 0.0007570743 0.002845546