如何解决解释p.adjust函数的结果
我使用以下代码使用“ ordered_pvs” 730x1向量执行了p.adjust函数：
ordered_pvs$Bonferroni = p.adjust(ordered_pvs$Raw_PVs,method = "bonferroni")
ordered_pvs$BH = p.adjust(ordered_pvs$Raw_PVs,method = "BH")
ordered_pvs$Holm = p.adjust(ordered_pvs$Raw_PVs,method = "holm")
ordered_pvs$Hochberg = p.adjust(ordered_pvs$Raw_PVs,method = "hochberg")
ordered_pvs$Hommel = p.adjust(ordered_pvs$Raw_PVs,method = "hommel")
ordered_pvs$BY = p.adjust(ordered_pvs$Raw_PVs,method = "BY")
以下是我从此函数得到的结果。我想知道如何解释这些结果？例如，当第73位的BH = 1和第75位的HOLM = 1时，这是什么意思？我将如何解释其他结果？
我一直在努力寻找任何研究论文等来帮助我。
Raw_PVs rank critical_value Bonferroni BH Holm Hochberg Hommel BY
0.000000 31 0.002123 0.00000 0.000000e+00 0.000000 0.000000 0.0000000 0.0000000000
0.000000 32 0.002192 0.00000 0.000000e+00 0.000000 0.000000 0.0000000 0.0000000000
0.000001 33 0.002260 0.00073 2.147059e-05 0.000698 0.000697 0.0006950 0.0001539644
0.000001 34 0.002329 0.00073 2.147059e-05 0.000698 0.000697 0.0006950 0.0001539644
0.000002 35 0.002397 0.00146 4.171429e-05 0.001392 0.001392 0.0013800 0.0002991308
0.000003 36 0.002466 0.00219 5.763158e-05 0.002085 0.002079 0.0020670 0.0004132729
0.000003 37 0.002534 0.00219 5.763158e-05 0.002085 0.002079 0.0020670 0.0004132729
0.000003 38 0.002603 0.00219 5.763158e-05 0.002085 0.002079 0.0020670 0.0004132729
0.000006 39 0.002671 0.00438 1.095000e-04 0.004152 0.004146 0.0041160 0.0007852185
0.000006 40 0.002740 0.00438 1.095000e-04 0.004152 0.004146 0.0041160 0.0007852185
0.000007 41 0.002808 0.00511 1.216667e-04 0.004830 0.004823 0.0047950 0.0008724650
0.000007 42 0.002877 0.00511 1.216667e-04 0.004830 0.004823 0.0047950 0.0008724650
0.000009 43 0.002945 0.00657 1.493182e-04 0.006192 0.006183 0.0061560 0.0010707525
0.000009 44 0.003014 0.00657 1.493182e-04 0.006192 0.006183 0.0061560 0.0010707525
0.000013 45 0.003082 0.00949 2.108889e-04 0.008918 0.008918 0.0088790 0.0015122726
0.000017 46 0.003151 0.01241 2.697826e-04 0.011645 0.011645 0.0116110 0.0019345962
0.000020 47 0.003219 0.01460 3.106383e-04 0.013680 0.013680 0.0136600 0.0022275701
0.000024 48 0.003288 0.01752 3.650000e-04 0.016392 0.016392 0.0163680 0.0026173949
0.000060 49 0.003356 0.04380 8.938776e-04 0.040920 0.040920 0.0406800 0.0064099467
0.000077 50 0.003425 0.05621 1.124200e-03 0.052437 0.052437 0.0518980 0.0080615763
0.000089 51 0.003493 0.06497 1.273922e-03 0.060520 0.060520 0.0597190 0.0091352215
0.000148 52 0.003562 0.10804 2.077692e-03 0.100492 0.100492 0.0984200 0.0148990172
0.000162 53 0.003630 0.11826 2.231321e-03 0.109836 0.109836 0.1075680 0.0160006784
0.000289 54 0.003699 0.21097 3.906852e-03 0.195653 0.195653 0.1907400 0.0280158197
0.000305 55 0.003767 0.22265 4.048182e-03 0.206180 0.206180 0.2003850 0.0290292891
0.000315 56 0.003836 0.22995 4.106250e-03 0.212625 0.212625 0.2063250 0.0294456928
0.000338 57 0.003904 0.24674 4.328772e-03 0.227812 0.227812 0.2197000 0.0310413853
0.000524 58 0.003973 0.38252 6.595172e-03 0.352652 0.352652 0.3285480 0.0472936185
0.000686 59 0.004041 0.50078 8.431500e-03 0.460992 0.460992 0.4198320 0.0604618225
0.000693 60 0.004110 0.50589 8.431500e-03 0.465003 0.465003 0.4234230 0.0604618225
0.000711 61 0.004178 0.51903 8.508689e-03 0.476370 0.476370 0.4337100 0.0610153372
0.000731 62 0.004247 0.53363 8.606935e-03 0.489039 0.489039 0.4444480 0.0617198608
0.000767 63 0.004315 0.55991 8.887460e-03 0.512356 0.512356 0.4625010 0.0637314889
0.000780 64 0.004384 0.56940 8.896875e-03 0.520260 0.520260 0.4687800 0.0637990011
0.000807 65 0.004452 0.58911 9.063231e-03 0.537462 0.537462 0.4825860 0.0649919291
0.000864 66 0.004521 0.63072 9.556364e-03 0.574560 0.574560 0.5106240 0.0685281578
0.000931 67 0.004589 0.67963 1.014373e-02 0.618184 0.618184 0.5427730 0.0727401393
0.001001 68 0.004658 0.73073 1.074603e-02 0.663663 0.663663 0.5775770 0.0770591856
0.001099 69 0.004726 0.80227 1.162710e-02 0.727538 0.727538 0.6253310 0.0833773047
0.001229 70 0.004795 0.89717 1.281671e-02 0.812369 0.812369 0.6747210 0.0919079529
0.001296 71 0.004863 0.94608 1.330222e-02 0.855360 0.855360 0.6998400 0.0953895036
0.001312 72 0.004932 0.95776 1.330222e-02 0.864608 0.864608 0.7058560 0.0953895036
0.001393 73 0.005000 1.00000 1.392919e-02 0.916594 0.916594 0.7355040 0.0998854492
0.001412 74 0.005068 1.00000 1.392919e-02 0.927684 0.927684 0.7427120 0.0998854492
0.001678 75 0.005137 1.00000 1.633253e-02 1.000000 0.999309 0.8282769 0.1171196978
0.002392 76 0.005205 1.00000 2.297579e-02 1.000000 0.999309 0.9663680 0.1647581221
0.002783 77 0.005274 1.00000 2.607410e-02 1.000000 0.999309 0.9822422 0.1869759547
0.002786 78 0.005342 1.00000 2.607410e-02 1.000000 0.999309 0.9822422 0.1869759547
0.002966 79 0.005411 1.00000 2.740734e-02 1.000000 0.999309 0.9861329 0.1965365397
0.003544 80 0.005479 1.00000 3.233900e-02 1.000000 0.999309 0.9899314 0.2319011895
0.004110 81 0.005548 1.00000 3.704074e-02 1.000000 0.999309 0.9916178 0.2656171137
0.004585 82 0.005616 1.00000 4.081768e-02 1.000000 0.999309 0.9930539 0.2927013582
0.006034 83 0.005685 1.00000 5.307012e-02 1.000000 0.999309 0.9973494 0.3805629137
0.006139 84 0.005753 1.00000 5.335083e-02 1.000000 0.999309 0.9976873 0.3825758902
0.007263 85 0.005822 1.00000 6.237635e-02 1.000000 0.999309 0.9993090 0.4472973946
0.007686 86 0.005890 1.00000 6.524163e-02 1.000000 0.999309 0.9993090 0.4678441237
0.007949 87 0.005959 1.00000 6.669851e-02 1.000000 0.999309 0.9993090 0.4782913146
0.008247 88 0.006027 1.00000 6.772596e-02 1.000000 0.999309 0.9993090 0.4856590970
0.008257 89 0.006096 1.00000 6.772596e-02 1.000000 0.999309 0.9993090 0.4856590970
0.008733 90 0.006164 1.00000 7.083433e-02 1.000000 0.999309 0.9993090 0.5079491066
0.008877 91 0.006233 1.00000 7.089728e-02 1.000000 0.999309 0.9993090 0.5084005124
0.008935 92 0.006301 1.00000 7.089728e-02 1.000000 0.999309 0.9993090 0.5084005124
0.009793 93 0.006370 1.00000 7.686978e-02 1.000000 0.999309 0.9993090 0.5512289980
0.009972 94 0.006438 1.00000 7.713411e-02 1.000000 0.999309 0.9993090 0.5531244245
0.010038 95 0.006507 1.00000 7.713411e-02 1.000000 0.999309 0.9993090 0.5531244245
0.010260 96 0.006575 1.00000 7.801875e-02 1.000000 0.999309 0.9993090 0.5594681632
0.010469 97 0.006644 1.00000 7.878732e-02 1.000000 0.999309 0.9993090 0.5649795334
0.010678 98 0.006712 1.00000 7.954020e-02 1.000000 0.999309 0.9993090 0.5703784267
0.010962 99 0.006781 1.00000 8.083091e-02 1.000000 0.999309 0.9993090 0.5796340013
0.011441 100 0.006849 1.00000 8.198462e-02 1.000000 0.999309 0.9993090 0.5879071657
0.011445 101 0.006918 1.00000 8.198462e-02 1.000000 0.999309 0.9993090 0.5879071657
0.011564 102 0.006986 1.00000 8.198462e-02 1.000000 0.999309 0.9993090 0.5879071657
0.011610 103 0.007055 1.00000 8.198462e-02 1.000000 0.999309 0.9993090 0.5879071657
0.011680 104 0.007123 1.00000 8.198462e-02 1.000000 0.999309 0.9993090 0.5879071657
0.012043 105 0.007192 1.00000 8.372752e-02 1.000000 0.999309 0.9993090 0.6004054661
0.012260 106 0.007260 1.00000 8.443208e-02 1.000000 0.999309 0.9993090 0.6054577673
0.012495 107 0.007329 1.00000 8.524626e-02 1.000000 0.999309 0.9993090 0.6112962518
0.012959 108 0.007397 1.00000 8.649173e-02 1.000000 0.999309 0.9993090 0.6202274170
0.012984 109 0.007466 1.00000 8.649173e-02 1.000000 0.999309 0.9993090 0.6202274170
0.013033 110 0.007534 1.00000 8.649173e-02 1.000000 0.999309 0.9993090 0.6202274170
0.013176 111 0.007603 1.00000 8.665297e-02 1.000000 0.999309 0.9993090 0.6213837010
非常感谢您！
解决方法
如果您阅读本手册：
前四种方法旨在对 家庭错误率。
如果方法位于c("holm","hochberg","hommel","bonferroni")中，则调整后的p值是调整后的alpha（有意义的阈值），此项对于该输入将是有意义的。例如，
p = c(0.001,0.005,0.01,0.22,0.33,0.44,0.55,0.66,0.77,0.88)
α（显着性水平）= 0.05的Bonferroni校正表示有意义，需要小于0.05 / 10 = 0.005，这意味着您将一个或多个I型错误的可能性控制在0.05。
如果我们使用p.adjust，您会看到只有第一个在0.05的alpha处有意义：
p.adjust(p,"bonferroni")
[1] 0.01 0.05 0.10 1.00 1.00 1.00 1.00 1.00 1.00 1.00
对于其他FDR方法，这些方法反映了条目将通过的最低FDR截止值，例如：
p.adjust(p,"BH")
[1] 0.01000000 0.02500000 0.03333333 0.55000000 0.66000000 0.73333333
[7] 0.78571429 0.82500000 0.85555556 0.88000000
因此，如果我们将FDR截止值设置为0.05，这意味着我们允许5％的I型错误，那么我们将声明前三个条目的FDR为5％，因为前3个条目的
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