如何在ROCit中计算ROC？

我想用ROCit来创建ROC曲线。我可以改变方向来计算ROC曲线吗 （高值与健康有关）

由于问题中没有示例，我将从

？rocit

文档中的示例开始，如果我误解了您的问题，请告诉我

# Load some example data
data("Diabetes")

# Calculate some ROC/validation data
roc_empirical <- rocit(score = Diabetes$chol, class = Diabetes$dtest,
                       negref = "-") # default method empirical
roc_binormal <- rocit(score = Diabetes$chol, class = Diabetes$dtest,
                      negref = "-", method = "bin")

# Summarize and plot the results 
summary(roc_empirical) #60/329
summary(roc_binormal) 
plot(roc_empirical)
plot(roc_binormal, col = c("#00BA37", "#F8766D"),
     legend = FALSE, YIndex = FALSE)

现在，如果我理解（？）您只是想翻转参考值的含义/方向，在本例中是

Diabetes$dtest

我们可以使用

negref

参数来实现这一点：

roc_empirical <- rocit(score = Diabetes$chol, class = Diabetes$dtest,
                       negref = "+") # default method empirical
roc_binormal <- rocit(score = Diabetes$chol, class = Diabetes$dtest,
                      negref = "+", method = "bin")

summary(roc_empirical)
summary(roc_binormal) 
plot(roc_empirical)
plot(roc_binormal, col = c("#00BA37", "#F8766D"),
     legend = FALSE, YIndex = FALSE)

当然，你也可以对有问题的专栏重新编码

# Load some example data
data("Diabetes")

# Calculate some ROC/validation data
roc_empirical <- rocit(score = Diabetes$chol, class = Diabetes$dtest,
                       negref = "-") # default method empirical
roc_binormal <- rocit(score = Diabetes$chol, class = Diabetes$dtest,
                      negref = "-", method = "bin")

# Summarize and plot the results 
summary(roc_empirical) #60/329
summary(roc_binormal) 
plot(roc_empirical)
plot(roc_binormal, col = c("#00BA37", "#F8766D"),
     legend = FALSE, YIndex = FALSE)

---

这就是你所需要的吗？

欢迎来到SO！如果你能为我们提供一个简单的例子（“”），那总是好的，因此，例如，当你说“高价值与健康相关”时，你所说的会更清楚。

roc_empirical <- rocit(score = Diabetes$chol, class = Diabetes$dtest,
                       negref = "+") # default method empirical
roc_binormal <- rocit(score = Diabetes$chol, class = Diabetes$dtest,
                      negref = "+", method = "bin")

summary(roc_empirical)
summary(roc_binormal) 
plot(roc_empirical)
plot(roc_binormal, col = c("#00BA37", "#F8766D"),
     legend = FALSE, YIndex = FALSE)

 Empirical ROC curve                  
 Number of postive responses :  329   
 Number of negative responses :  60   
 Area under curve :  0.353850050658561