使用Stata进行Logistic回归分析实例分析
2017-06-19 15:47
使用Stata进行Logistic回归分析实例分析,一下是具体的操作,简洁明了,一目了然.大家可以看看
use "C:\Stata12\2_data\002-胰腺炎.dta", clear
. sum
Variable | Obs Mean Std. Dev. Min Max
————-+——————————————————–
id | 113 785515.7 53014.54 605046 833486
sex | 113 .5486726 .4998419 0 1
age | 113 59.06195 18.07933 17 90
ldh | 113 433.5434 448.6421 2.9 2272
cr | 113 106.3265 100.756 21 775
————-+——————————————————–
abl | 113 34.45221 6.624105 17.9 51.2
mods | 113 .2477876 .4336509 0 1
pre | 113 .2477879 .3315311 .00382 .99995
. list
+————————————————————+
| id sex age ldh cr abl mods pre |
|————————————————————|
1. | 828966 0 65 299.3 47.1 34.4 1 .0614 |
2. | 769948 1 40 2036 395.1 25.9 1 .99972 |
3. | 691896 1 78 881 89.4 39.1 1 .17659 |
4. | 679641 1 79 2250 360.2 26.2 1 .99972 |
5. | 766834 1 79 300 775 22.4 1 .99995 |
|————————————————————|
6. | 746872 1 76 410 177 21.1 1 .86829 |
7. | 711428 1 58 2047.4 276 27.1 1 .99814 |
8. | 699401 0 62 633 235.4 24.7 1 .93165 |
9. | 789971 0 79 225 71 30.2 1 .1432 |
10. | 788979 1 21 1149 37 21 1 .85097 |
|————————————————————|
11. | 780270 1 59 881 310 34 1 .92918 |
12. | 775535 0 77 500 318 28 1 .94542 |
13. | 650668 1 57 1248 180 29 1 .92791 |
14. | 697919 1 84 345 210 32.7 1 .51026 |
15. | 699401 0 62 633 235 24.7 1 .93128 |
|————————————————————|
16. | 699767 0 76 460.5 157 26 1 .69305 |
17. | 728235 0 77 359 159 35.4 1 .23909 |
18. | 734791 0 84 305 138 17.9 1 .84005 |
19. | 738421 1 56 1487 306 27 1 .99519 |
20. | 746872 1 76 1211 205 27.2 1 .95914 |
|————————————————————|
21. | 763940 1 39 407 60 33.4 1 .11039 |
22. | 822913 0 41 1100 38 28.9 1 .54136 |
23. | 816293 1 77 506 92.7 40.9 1 .0593 |
24. | 820032 1 75 320 107 26.4 1 .41857 |
25. | 821686 1 45 823 63 18.8 1 .84678 |
|————————————————————|
26. | 831350 0 48 1402.3 318 28.2 1 .99376 |
27. | 829526 1 65 2272 383 21.6 1 .99992 |
28. | 830224 0 76 489.7 71 36.2 1 .09599 |
29. | 685639 0 80 245 123 36.2 0 .10833 |
30. | 798034 0 40 230 21 24.3 0 .19822 |
|————————————————————|
31. | 700759 0 46 264 51 30.9 0 .10826 |
32. | 616791 0 51 293 38 28.8 0 .13795 |
33. | 805107 1 79 168 52 28.7 0 .12727 |
34. | 805110 0 46 168 45 33.2 0 .05406 |
35. | 804010 1 78 224 56 28.2 0 .16314 |
|————————————————————|
36. | 801367 1 53 175 78 45 0 .01031 |
37. | 802216 0 76 290 87 32 0 .1504 |
38. | 803383 0 32 117 66 38.8 0 .02345 |
39. | 795567 0 44 147 58 39.7 0 .01915 |
40. | 794845 0 64 203 51 46.9 0 .0053 |
|————————————————————|
41. | 794119 1 39 189 84 41.6 0 .02164 |
42. | 794338 0 88 658 205 34.4 0 .60721 |
43. | 794131 0 60 210 46 41.3 0 .01409 |
44. | 794202 0 25 555 52 31.8 0 .17736 |
45. | 803426 0 57 264 58 41.8 0 .01739 |
|————————————————————|
46. | 806737 1 61 214 79 41 0 .02392 |
47. | 806539 1 65 181 70 36.5 0 .04376 |
48. | 806537 1 63 454 80 33.2 0 .16177 |
49. | 806023 1 56 319 67 38.3 0 .04241 |
50. | 802369 0 68 1033 88 32.2 0 .52563 |
|————————————————————|
51. | 802028 0 82 320 64 31.6 0 .12873 |
52. | 801515 1 35 171 73 37.2 0 .03931 |
53. | 801928 0 70 449 59 37.7 0 .05758 |
54. | 800184 0 85 278 55 35.2 0 .05649 |
55. | 801605 0 70 2.9 54 37.9 0 .01765 |
|————————————————————|
56. | 801603 0 35 354 30 37.9 0 .02971 |
57. | 801307 1 86 138 78 34.8 0 .05947 |
58. | 800230 0 77 225 53 36.1 0 .04133 |
59. | 794964 1 66 323 95 33.1 0 .14949 |
60. | 795620 1 43 146 87 36.5 0 .0508 |
|————————————————————|
61. | 795252 0 48 205 66 33.1 0 .07946 |
62. | 795526 1 48 174 94 41 0 .02676 |
63. | 792978 0 58 170 72 35.2 0 .05513 |
64. | 794217 1 57 270 58 33.9 0 .07237 |
65. | 773257 0 76 160 63 35.2 0 .04763 |
|————————————————————|
66. | 792542 1 49 194 57 32.7 0 .07364 |
67. | 792833 1 47 158 94 34.5 0 .08124 |
68. | 800538 1 66 217 50 36.6 0 .03558 |
69. | 789694 1 85 310 76 27.7 0 .26112 |
70. | 799492 0 72 29 40 29 0 .07581 |
|————————————————————|
71. | 793578 0 72 186 71 31 0 .11556 |
72. | 791232 0 77 144 61 34.8 0 .04788 |
73. | 788760 1 57 145 90 47.6 0 .00703 |
74. | 799116 1 44 227 61 37.3 0 .03743 |
75. | 802375 1 49 279 63 45.3 0 .0102 |
|————————————————————|
76. | 784337 1 32 148 64 35.6 0 .04371 |
77. | 783947 1 31 269 76 40.8 0 .02719 |
78. | 783842 1 29 654 74 36 0 .14782 |
79. | 783501 1 69 236 74 44 0 .01361 |
80. | 783198 1 84 203 60 37.7 0 .03243 |
|————————————————————|
81. | 605046 1 35 1194 204 38.1 0 .74518 |
82. | 610769 0 55 982 50 30.4 0 .44136 |
83. | 619327 1 17 485 83 41.8 0 .04217 |
84. | 650544 0 74 258 212 31 0 .54198 |
85. | 767680 0 70 290.3 80 39.7 0 .03689 |
|————————————————————|
86. | 829694 1 28 265 73 51.2 0 .00382 |
87. | 829106 0 59 337 48 35.5 0 .05603 |
88. | 828745 1 38 218 74 43.8 0 .0135 |
89. | 828666 1 89 498 101 39.1 0 .08864 |
90. | 828263 1 50 187 74 28.5 0 .17874 |
|————————————————————|
91. | 827393 1 77 186 69 42.3 0 .01531 |
92. | 827369 1 62 242 90 37.9 0 .05191 |
93. | 827156 0 25 282 54 41.7 0 .0175 |
94. | 827034 0 27 144 49 30 0 .09364 |
95. | 826948 0 34 124 48 42 0 .01031 |
|————————————————————|
96. | 826817 1 34 202 70 37.7 0 .03716 |
97. | 826696 1 58 303 70 33 0 .10633 |
98. | 825045 1 63 234 61 30.5 0 .12284 |
99. | 824940 0 80 271 71 40.9 0 .02502 |
100. | 824605 1 38 157 87 47.7 0 .00681 |
|————————————————————|
101. | 823381 1 70 209 74 31 0 .12624 |
102. | 833486 0 72 168 94 27.6 0 .24636 |
103. | 832515 0 90 193 45 30.8 0 .08678 |
104. | 832070 1 50 219 80 35.9 0 .06098 |
105. | 831928 1 37 131 79 43.5 0 .01236 |
|————————————————————|
106. | 831566 0 62 179 61 41 0 .01704 |
107. | 831124 0 65 235 45 35.6 0 .04146 |
108. | 830946 1 55 115 71 44.9 0 .00819 |
109. | 830745 1 45 134 78 39.7 0 .02456 |
110. | 830581 1 67 369 73 39.2 0 .04423 |
|————————————————————|
111. | 830523 0 63 967 81 34.8 0 .34388 |
112. | 829833 0 75 184 89 39.7 0 .03233 |
113. | 828503 1 29 662 96 26.4 0 .59103 |
+————————————————————+
. logit mods sex
Iteration 0: log likelihood = -63.26774
Iteration 1: log likelihood = -63.009407
Iteration 2: log likelihood = -63.008974
Iteration 3: log likelihood = -63.008974
Logistic regression Number of obs = 113
LR chi2(1) = 0.52
Prob > chi2 = 0.4719
Log likelihood = -63.008974 Pseudo R2 = 0.0041
——————————————————————————
mods | Coef. Std. Err. z P>|z| [95% Conf. Interval]
————-+—————————————————————-
sex | .317535 .4437959 0.72 0.474 -.552289 1.187359
_cons | -1.290984 .3404542 -3.79 0.000 -1.958262 -.6237061
——————————————————————————
. logit mods age
Iteration 0: log likelihood = -63.26774
Iteration 1: log likelihood = -61.410619
Iteration 2: log likelihood = -61.384146
Iteration 3: log likelihood = -61.384131
Iteration 4: log likelihood = -61.384131
Logistic regression Number of obs = 113
LR chi2(1) = 3.77
Prob > chi2 = 0.0523
Log likelihood = -61.384131 Pseudo R2 = 0.0298
——————————————————————————
mods | Coef. Std. Err. z P>|z| [95% Conf. Interval]
————-+—————————————————————-
age | .0246326 .0131484 1.87 0.061 -.0011379 .050403
_cons | -2.614525 .8575939 -3.05 0.002 -4.295378 -.9336716
——————————————————————————
. logit mods ldh
Iteration 0: log likelihood = -63.26774
Iteration 1: log likelihood = -43.576347
Iteration 2: log likelihood = -43.455543
Iteration 3: log likelihood = -43.455308
Iteration 4: log likelihood = -43.455308
Logistic regression Number of obs = 113
LR chi2(1) = 39.62
Prob > chi2 = 0.0000
Log likelihood = -43.455308 Pseudo R2 = 0.3132
——————————————————————————
mods | Coef. Std. Err. z P>|z| [95% Conf. Interval]
————-+—————————————————————-
ldh | .0040724 .0009141 4.45 0.000 .0022808 .0058641
_cons | -3.006031 .4828876 -6.23 0.000 -3.952473 -2.059589
——————————————————————————
. logit mods cr
Iteration 0: log likelihood = -63.26774
Iteration 1: log likelihood = -41.24542
Iteration 2: log likelihood = -41.119546
Iteration 3: log likelihood = -41.117441
Iteration 4: log likelihood = -41.117441
Logistic regression Number of obs = 113
LR chi2(1) = 44.30
Prob > chi2 = 0.0000
Log likelihood = -41.117441 Pseudo R2 = 0.3501
——————————————————————————
mods | Coef. Std. Err. z P>|z| [95% Conf. Interval]
————-+—————————————————————-
cr | .0225873 .0050643 4.46 0.000 .0126615 .0325131
_cons | -3.578768 .5798729 -6.17 0.000 -4.715298 -2.442238
——————————————————————————
. logit mods abl
Iteration 0: log likelihood = -63.26774
Iteration 1: log likelihood = -45.365845
Iteration 2: log likelihood = -43.453786
Iteration 3: log likelihood = -43.421114
Iteration 4: log likelihood = -43.421108
Iteration 5: log likelihood = -43.421108
Logistic regression Number of obs = 113
LR chi2(1) = 39.69
Prob > chi2 = 0.0000
Log likelihood = -43.421108 Pseudo R2 = 0.3137
——————————————————————————
mods | Coef. Std. Err. z P>|z| [95% Conf. Interval]
————-+—————————————————————-
abl | -.2767854 .0579555 -4.78 0.000 -.3903761 -.1631947
_cons | 7.821677 1.815949 4.31 0.000 4.262483 11.38087
——————————————————————————
. logit mods ldh cr abl
Iteration 0: log likelihood = -63.26774
Iteration 1: log likelihood = -31.249401
Iteration 2: log likelihood = -30.061031
Iteration 3: log likelihood = -30.03929
Iteration 4: log likelihood = -30.039258
Iteration 5: log likelihood = -30.039258
Logistic regression Number of obs = 113
LR chi2(3) = 66.46
Prob > chi2 = 0.0000
Log likelihood = -30.039258 Pseudo R2 = 0.5252
——————————————————————————
mods | Coef. Std. Err. z P>|z| [95% Conf. Interval]
————-+—————————————————————-
ldh | .0024992 .001073 2.33 0.020 .0003962 .0046021
cr | .0143511 .0057272 2.51 0.012 .0031261 .0255761
abl | -.1858638 .0647696 -2.87 0.004 -.3128099 -.0589177
_cons | 2.24286 2.246818 1.00 0.318 -2.160823 6.646544
——————————————————————————
. lfit,g(10)
Logistic model for mods, goodness-of-fit test
(Table collapsed on quantiles of estimated probabilities)
number of observations = 113
number of groups = 10
Hosmer-Lemeshow chi2(8) = 5.93
Prob > chi2 = 0.6549
. lstat
Logistic model for mods
——– True ——–
Classified | D ~D | Total
———–+————————–+———–
+ | 20 5 | 25
– | 8 80 | 88
———–+————————–+———–
Total | 28 85 | 113
Classified + if predicted Pr(D) >= .5
True D defined as mods != 0
————————————————–
Sensitivity Pr( +| D) 71.43%
Specificity Pr( -|~D) 94.12%
Positive predictive value Pr( D| +) 80.00%
Negative predictive value Pr(~D| -) 90.91%
————————————————–
False + rate for true ~D Pr( +|~D) 5.88%
False – rate for true D Pr( -| D) 28.57%
False + rate for classified + Pr(~D| +) 20.00%
False – rate for classified – Pr( D| -) 9.09%
————————————————–
Correctly classified 88.50%
————————————————–
. predict pre
(option pr assumed; Pr(mods))
. roctab mods pre
ROC -Asymptotic Normal–
Obs Area Std. Err. [95% Conf. Interval]
——————————————————–
113 0.9273 0.0268 0.87485 0.97977
. roctab mods pre,g
![使用Stata进行Logistic回归分析实例分析]( "使用Stata进行Logistic回归分析实例分析")
. lsens
![使用Stata进行Logistic回归分析实例分析]( "使用Stata进行Logistic回归分析实例分析")
. roccomp mods pre ldh cr abl
ROC -Asymptotic Normal–
Obs Area Std. Err. [95% Conf. Interval]
————————————————————————-
pre 113 0.9273 0.0268 0.87485 0.97977
ldh 113 0.9034 0.0285 0.84752 0.95921
cr 113 0.7998 0.0633 0.67580 0.92378
abl 113 0.1483 0.0444 0.06136 0.23528
————————————————————————-
Ho: area(pre) = area(ldh) = area(cr) = area(abl)
chi2(3) = 189.39 Prob>chi2 = 0.0000
. rocgold mods pre ldh cr abl
——————————————————————————-
ROC Bonferroni
Area Std. Err. chi2 df Pr>chi2 Pr>chi2
——————————————————————————-
pre (standard) 0.9273 0.0268
ldh 0.9034 0.0285 0.6873 1 0.4071 1.0000
cr 0.7998 0.0633 4.9712 1 0.0258 0.0773
abl 0.1483 0.0444 135.4836 1 0.0000 0.0000
——————————————————————————-
. roctab mods pre,d
Detailed report of sensitivity and specificity
——————————————————————————
Correctly
Cutpoint Sensitivity Specificity Classified LR+ LR-
——————————————————————————
( >= .00382 ) 100.00% 0.00% 24.78% 1.0000
( >= .0053 ) 100.00% 1.18% 25.66% 1.0119 0.0000
( >= .00681 ) 100.00% 2.35% 26.55% 1.0241 0.0000
( >= .00703 ) 100.00% 3.53% 27.43% 1.0366 0.0000
( >= .00819 ) 100.00% 4.71% 28.32% 1.0494 0.0000
( >= .0102 ) 100.00% 5.88% 29.20% 1.0625 0.0000
( >= .01031 ) 100.00% 7.06% 30.09% 1.0759 0.0000
( >= .01236 ) 100.00% 9.41% 31.86% 1.1039 0.0000
( >= .0135 ) 100.00% 10.59% 32.74% 1.1184 0.0000
( >= .01361 ) 100.00% 11.76% 33.63% 1.1333 0.0000
( >= .01409 ) 100.00% 12.94% 34.51% 1.1486 0.0000
( >= .01531 ) 100.00% 14.12% 35.40% 1.1644 0.0000
( >= .01704 ) 100.00% 15.29% 36.28% 1.1806 0.0000
( >= .01739 ) 100.00% 16.47% 37.17% 1.1972 0.0000
( >= .0175 ) 100.00% 17.65% 38.05% 1.2143 0.0000
( >= .01765 ) 100.00% 18.82% 38.94% 1.2319 0.0000
( >= .01915 ) 100.00% 20.00% 39.82% 1.2500 0.0000
( >= .02164 ) 100.00% 21.18% 40.71% 1.2687 0.0000
( >= .02345 ) 100.00% 22.35% 41.59% 1.2879 0.0000
( >= .02392 ) 100.00% 23.53% 42.48% 1.3077 0.0000
( >= .02456 ) 100.00% 24.71% 43.36% 1.3281 0.0000
( >= .02502 ) 100.00% 25.88% 44.25% 1.3492 0.0000
( >= .02676 ) 100.00% 27.06% 45.13% 1.3710 0.0000
( >= .02719 ) 100.00% 28.24% 46.02% 1.3934 0.0000
( >= .02971 ) 100.00% 29.41% 46.90% 1.4167 0.0000
( >= .03233 ) 100.00% 30.59% 47.79% 1.4407 0.0000
( >= .03243 ) 100.00% 31.76% 48.67% 1.4655 0.0000
( >= .03558 ) 100.00% 32.94% 49.56% 1.4912 0.0000
( >= .03689 ) 100.00% 34.12% 50.44% 1.5179 0.0000
( >= .03716 ) 100.00% 35.29% 51.33% 1.5455 0.0000
( >= .03743 ) 100.00% 36.47% 52.21% 1.5741 0.0000
( >= .03931 ) 100.00% 37.65% 53.10% 1.6038 0.0000
( >= .04133 ) 100.00% 38.82% 53.98% 1.6346 0.0000
( >= .04146 ) 100.00% 40.00% 54.87% 1.6667 0.0000
( >= .04217 ) 100.00% 41.18% 55.75% 1.7000 0.0000
( >= .04241 ) 100.00% 42.35% 56.64% 1.7347 0.0000
( >= .04371 ) 100.00% 43.53% 57.52% 1.7708 0.0000
( >= .04376 ) 100.00% 44.71% 58.41% 1.8085 0.0000
( >= .04423 ) 100.00% 45.88% 59.29% 1.8478 0.0000
( >= .04763 ) 100.00% 47.06% 60.18% 1.8889 0.0000
( >= .04788 ) 100.00% 48.24% 61.06% 1.9318 0.0000
( >= .0508 ) 100.00% 49.41% 61.95% 1.9767 0.0000
( >= .05191 ) 100.00% 50.59% 62.83% 2.0238 0.0000
( >= .05406 ) 100.00% 51.76% 63.72% 2.0732 0.0000
( >= .05513 ) 100.00% 52.94% 64.60% 2.1250 0.0000
( >= .05603 ) 100.00% 54.12% 65.49% 2.1795 0.0000
( >= .05649 ) 100.00% 55.29% 66.37% 2.2368 0.0000
( >= .05758 ) 100.00% 56.47% 67.26% 2.2973 0.0000
( >= .0593 ) 100.00% 57.65% 68.14% 2.3611 0.0000
( >= .05947 ) 96.43% 57.65% 67.26% 2.2768 0.0620
( >= .06098 ) 96.43% 58.82% 68.14% 2.3418 0.0607
( >= .0614 ) 96.43% 60.00% 69.03% 2.4107 0.0595
( >= .07237 ) 92.86% 60.00% 68.14% 2.3214 0.1190
( >= .07364 ) 92.86% 61.18% 69.03% 2.3918 0.1168
( >= .07581 ) 92.86% 62.35% 69.91% 2.4665 0.1146
( >= .07946 ) 92.86% 63.53% 70.80% 2.5461 0.1124
( >= .08124 ) 92.86% 64.71% 71.68% 2.6310 0.1104
( >= .08678 ) 92.86% 65.88% 72.57% 2.7217 0.1084
( >= .08864 ) 92.86% 67.06% 73.45% 2.8189 0.1065
( >= .09364 ) 92.86% 68.24% 74.34% 2.9233 0.1047
( >= .09599 ) 92.86% 69.41% 75.22% 3.0357 0.1029
( >= .10633 ) 89.29% 69.41% 74.34% 2.9190 0.1544
( >= .10826 ) 89.29% 70.59% 75.22% 3.0357 0.1518
( >= .10833 ) 89.29% 71.76% 76.11% 3.1622 0.1493
( >= .11039 ) 89.29% 72.94% 76.99% 3.2997 0.1469
( >= .11556 ) 85.71% 72.94% 76.11% 3.1677 0.1959
( >= .12284 ) 85.71% 74.12% 76.99% 3.3117 0.1927
( >= .12624 ) 85.71% 75.29% 77.88% 3.4694 0.1897
( >= .12727 ) 85.71% 76.47% 78.76% 3.6429 0.1868
( >= .12873 ) 85.71% 77.65% 79.65% 3.8346 0.1840
( >= .13795 ) 85.71% 78.82% 80.53% 4.0476 0.1812
( >= .1432 ) 85.71% 80.00% 81.42% 4.2857 0.1786
( >= .14782 ) 82.14% 80.00% 80.53% 4.1071 0.2232
( >= .14949 ) 82.14% 81.18% 81.42% 4.3638 0.2200
( >= .1504 ) 82.14% 82.35% 82.30% 4.6548 0.2168
( >= .16177 ) 82.14% 83.53% 83.19% 4.9872 0.2138
( >= .16314 ) 82.14% 84.71% 84.07% 5.3709 0.2108
( >= .17659 ) 82.14% 85.88% 84.96% 5.8185 0.2079
( >= .17736 ) 78.57% 85.88% 84.07% 5.5655 0.2495
( >= .17874 ) 78.57% 87.06% 84.96% 6.0714 0.2461
( >= .19822 ) 78.57% 88.24% 85.84% 6.6786 0.2429
( >= .23909 ) 78.57% 89.41% 86.73% 7.4206 0.2397
( >= .24636 ) 75.00% 89.41% 85.84% 7.0833 0.2796
( >= .26112 ) 75.00% 90.59% 86.73% 7.9687 0.2760
( >= .34388 ) 75.00% 91.76% 87.61% 9.1071 0.2724
( >= .41857 ) 75.00% 92.94% 88.50% 10.6250 0.2690
( >= .44136 ) 71.43% 92.94% 87.61% 10.1190 0.3074
( >= .51026 ) 71.43% 94.12% 88.50% 12.1429 0.3036
( >= .52563 ) 67.86% 94.12% 87.61% 11.5357 0.3415
( >= .54136 ) 67.86% 95.29% 88.50% 14.4197 0.3373
( >= .54198 ) 64.29% 95.29% 87.61% 13.6607 0.3748
( >= .59103 ) 64.29% 96.47% 88.50% 18.2143 0.3702
( >= .60721 ) 64.29% 97.65% 89.38% 27.3214 0.3657
( >= .69305 ) 64.29% 98.82% 90.27% 54.6430 0.3614
( >= .74518 ) 60.71% 98.82% 89.38% 51.6073 0.3975
( >= .84005 ) 60.71% 100.00% 90.27% 0.3929
( >= .84678 ) 57.14% 100.00% 89.38% 0.4286
( >= .85097 ) 53.57% 100.00% 88.50% 0.4643
( >= .86829 ) 50.00% 100.00% 87.61% 0.5000
( >= .92791 ) 46.43% 100.00% 86.73% 0.5357
( >= .92918 ) 42.86% 100.00% 85.84% 0.5714
( >= .93128 ) 39.29% 100.00% 84.96% 0.6071
( >= .93165 ) 35.71% 100.00% 84.07% 0.6429
( >= .94542 ) 32.14% 100.00% 83.19% 0.6786
( >= .95914 ) 28.57% 100.00% 82.30% 0.7143
( >= .99376 ) 25.00% 100.00% 81.42% 0.7500
( >= .99519 ) 21.43% 100.00% 80.53% 0.7857
( >= .99814 ) 17.86% 100.00% 79.65% 0.8214
( >= .99972 ) 14.29% 100.00% 78.76% 0.8571
( >= .99992 ) 7.14% 100.00% 76.99% 0.9286
( >= .99995 ) 3.57% 100.00% 76.11% 0.9643
( > .99995 ) 0.00% 100.00% 75.22% 1.0000
——————————————————————————
精选的每一篇文章均来源于公开网络，仅供学习使用，不会用于任何商业用途，文章版权归原作者所有，如果侵犯到原作者的权益，请您与我们联系删除或者授权事宜，。转载网站文章请注明原文章作者，否则产生的任何版权纠纷与无关。
