为什么通过pROC Package的不同功能计算出的95％CI值不同？

我正在使用pROC软件包来计算特异性值和“最佳”阈值的95％CI，我的程序代码如下：

data(aSAH)
myroc <- roc(aSAH$outcome, aSAH$s100b)
ci.thresholds(myroc, thresholds = "best")

95% CI (2000 stratified bootstrap replicates):
 thresholds sp.low sp.median sp.high se.low se.median se.high
      0.205 0.7083    0.8056  0.8889 0.4878    0.6341  0.7805

我从函数ci.coords获得的值是：

ci.coords(myroc, x = "best", ret = c("specificity"))
95% CI (2000 stratified bootstrap replicates):
 threshold specificity.low specificity.median specificity.high
      best          0.6663             0.8194           0.9865

通过函数ci.thresholds得出的值为：

ci.thresholds(myroc)
95% CI (2000 stratified bootstrap replicates):
 thresholds  sp.low sp.median sp.high se.low se.median se.high
       -Inf 0.00000    0.0000  0.0000 1.0000    1.0000  1.0000
      0.065 0.06944    0.1389  0.2222 0.9268    0.9756  1.0000
      0.075 0.12500    0.2222  0.3194 0.8049    0.9024  0.9756
      0.085 0.19440    0.3056  0.4167 0.7805    0.8780  0.9756
      0.095 0.27780    0.3889  0.5000 0.7073    0.8293  0.9268
      0.105 0.37500    0.4861  0.5972 0.6579    0.7805  0.9024
      0.115 0.43060    0.5417  0.6528 0.6098    0.7561  0.8780
      0.135 0.47220    0.5833  0.6944 0.5366    0.6829  0.8293
      0.155 0.58330    0.6944  0.7917 0.5122    0.6585  0.8049
      0.205 0.70830    0.8056  0.8889 0.4878    0.6341  0.7805
      0.245 0.72220    0.8194  0.9028 0.4390    0.5854  0.7317
      0.290 0.75000    0.8333  0.9167 0.3659    0.5122  0.6585
      0.325 0.76390    0.8472  0.9306 0.3171    0.4634  0.6098
      0.345 0.79170    0.8750  0.9444 0.2927    0.4390  0.5854
      0.395 0.81910    0.8889  0.9583 0.2683    0.4146  0.5610
      0.435 0.83330    0.9028  0.9583 0.2439    0.3902  0.5366
      0.475 0.90280    0.9583  1.0000 0.1951    0.3415  0.4878
      0.485 0.93060    0.9722  1.0000 0.1707    0.3171  0.4634
      0.510 1.00000    1.0000  1.0000 0.1707    0.2927  0.4390

当阈值为0.205时，特异性值为0.8056（ci.thresholds（myroc，thresholds =“ best”）），但通过ci.coords（myroc，x =“ best”，ret = c（“ specificity” ））为0.8194，此时阈值为0.245。为什么通过不同功能获得的阈值不相同？

然后，ci.coords（myroc，x =“ best”，ret = c（“ specificity”））获得的特异性值为0.8194，95％CI为0.6806-0.9861，但通过ci.thresholds获得（myroc）为0.8194，95％CI：0.7222-0.9028。

更新：

> coords(myroc, x = "best", ret="all", transpose = FALSE)
          threshold specificity sensitivity  accuracy tn tp fn fp       npv  ppv  fdr       fpr       tpr       tnr
threshold     0.205   0.8055556   0.6341463 0.7433628 58 26 15 14 0.7945205 0.65 0.35 0.1944444 0.6341463 0.8055556
                fnr 1-specificity 1-sensitivity 1-accuracy     1-npv 1-ppv precision    recall   youden
threshold 0.3658537     0.1944444     0.3658537  0.2566372 0.2054795  0.35      0.65 0.6341463 1.439702
          closest.topleft
threshold       0.1716575



> ci.coords(myroc, x = "best", ret = "all", transpose = TRUE)
95% CI (2000 stratified bootstrap replicates):
     threshold threshold.low threshold.median threshold.high specificity.low specificity.median specificity.high
best      best          0.12            0.205           0.51          0.6663             0.8194                1
     sensitivity.low sensitivity.median sensitivity.high accuracy.low accuracy.median accuracy.high tn.low tn.median
best          0.3902             0.6341           0.8049       0.6637          0.7522         0.823  47.98        59
     tn.high tp.low tp.median tp.high fn.low fn.median fn.high fp.low fp.median fp.high npv.low npv.median npv.high
best      72     16        26      33      8        15      25      0        13   24.02  0.7273     0.7973   0.8732
     ppv.low ppv.median ppv.high fdr.low fdr.median fdr.high fpr.low fpr.median fpr.high tpr.low tpr.median tpr.high
best  0.5366     0.6667        1       0     0.3333   0.4634       0     0.1806   0.3337  0.3902     0.6341   0.8049
     tnr.low tnr.median tnr.high fnr.low fnr.median fnr.high 1-specificity.low 1-specificity.median 1-specificity.high
best  0.6663     0.8194        1  0.1951     0.3659   0.6098                 0               0.1806             0.3337
     1-sensitivity.low 1-sensitivity.median 1-sensitivity.high 1-accuracy.low 1-accuracy.median 1-accuracy.high
best            0.1951               0.3659             0.6098          0.177            0.2478          0.3363
     1-npv.low 1-npv.median 1-npv.high 1-ppv.low 1-ppv.median 1-ppv.high precision.low precision.median precision.high
best    0.1268       0.2027     0.2727         0       0.3333     0.4634        0.5366           0.6667              1
     recall.low recall.median recall.high youden.low youden.median youden.high closest.topleft.low
best     0.3902        0.6341      0.8049      1.279         1.447        1.61             0.08148
     closest.topleft.median closest.topleft.high
best                 0.1717               0.4021

coords和ci.coords的特异性分别为0.8055556和0.8194，上面还有其他一些不同的结果。