曲线下的R逻辑回归区域

I am performing logistic regression using this page. My code is as below.

我正在使用此页面执行逻辑回归。我的代码如下。

mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")
mylogit <- glm(admit ~ gre, data = mydata, family = "binomial")
summary(mylogit)
prob=predict(mylogit,type=c("response"))
mydata$prob=prob

After running this code mydata dataframe has two columns - 'admit' and 'prob'. Shouldn't those two columns sufficient to get the ROC curve?

运行此代码后，mydata dataframe有两列 - 'admit'和'prob'。这两列不应该足以获得ROC曲线吗？

How can I get the ROC curve.

如何获得ROC曲线。

Secondly, by loooking at mydata, it seems that model is predicting probablity of admit=1.

其次，通过在mydata上闲逛，模型似乎预测了admit = 1的可能性。

Is that correct?

那是对的吗？

How to find out which particular event the model is predicting?

如何找出模型预测的特定事件？

Thanks

谢谢

UPDATE: It seems that below three commands are very useful. They provide the cut-off which will have maximum accuracy and then help to get the ROC curve.

更新：似乎以下三个命令非常有用。它们提供了最大精度的截止点，然后有助于获得ROC曲线。

coords(g, "best")

mydata$prediction=ifelse(prob>=0.3126844,1,0)

confusionMatrix(mydata$prediction,mydata$admit

3 个解决方案

#1

The ROC curve compares the rank of prediction and answer. Therefore, you could evaluate the ROC curve with package pROC as follow:

ROC曲线比较预测和答案的等级。因此，您可以使用包pROC评估ROC曲线，如下所示：

mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")
mylogit <- glm(admit ~ gre, data = mydata, family = "binomial")
summary(mylogit)
prob=predict(mylogit,type=c("response"))
mydata$prob=prob
library(pROC)
g <- roc(admit ~ prob, data = mydata)
plot(g)

#2

another way to plot ROC Curve...

另一种绘制ROC曲线的方法......

library(Deducer)
modelfit <- glm(formula=admit ~ gre + gpa, family=binomial(), data=mydata, na.action=na.omit)
rocplot(modelfit)

#3

#Another way to plot ROC

mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")   
mylogit <- glm(admit ~ gre, data = mydata, family = "binomial")    
summary(mylogit)     
prob=predict(mylogit,type=c("response"))    
library("ROCR")    
pred <- prediction(prob, mydata$admit)    
perf <- performance(pred, measure = "tpr", x.measure = "fpr")     
plot(perf, col=rainbow(7), main="ROC curve Admissions", xlab="Specificity", 
     ylab="Sensitivity")    
abline(0, 1) #add a 45 degree line

#1