zhuang xiaojin
8月-16-2021
Step 1: Load the Data
rm(list = ls())
data("iris")
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
Step 2: Fit the Logistic Regression Model
#make this example reproducible
set.seed(1)
#Use 70% of dataset as training set and remaining 30% as testing set
sample <- sample(c(TRUE, FALSE), nrow(iris), replace=TRUE, prob=c(0.6,0.3))
train <- iris[sample, ]
test <- iris[!sample, ]
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
paste(names(iris),collapse = "+")
## [1] "Sepal.Length+Sepal.Width+Petal.Length+Petal.Width+Species"
#fit logistic regression model
model <- glm(Species~Sepal.Length+Sepal.Width+Petal.Length+Petal.Width,
family="binomial",
data=train)
Step 3: Calculate the AUC of the Model
接下来，我们将使用pROC包中的auc()函数来计算模型的 AUC。此函数使用以下语法：
auc(response, predicted)
以下是在我们的示例中如何使用此函数：
#calculate probability of default for each individual in test dataset
predicted <- predict(model, test, type="response")
#calculate AUC
library(pROC)
roc1 <- roc(test$Species,predicted);roc1 # Build a ROC object and compute the AUC
##
## Call:
## roc.default(response = test$Species, predictor = predicted)
##
## Data: predicted in 19 controls (test$Species setosa) < 16 cases (test$Species versicolor).
## Area under the curve: 1
auc(test$Species, predicted)
## Area under the curve: 1
plot(x = roc(response = test$Species, predictor = predicted,
percent = TRUE, ci = TRUE, of = "se",
sp = seq(0, 100, 5)), ci.type="shape")
image.png
plot(roc1, # roc1换为roc2，更改参数可绘制roc2曲线
print.auc=TRUE,print.auc.x=0.5,print.auc.y=0.5, # 图像上输出AUC值,坐标为（x，y）
auc.polygon=TRUE, auc.polygon.col="skyblue", # 设置ROC曲线下填充色
max.auc.polygon=TRUE, # 填充整个图像
grid=c(0.1,0.2), grid.col=c("green", "red"), # 设置间距为0.1，0.2，线条颜色
print.thres=TRUE, print.thres.cex=0.8, # 图像上输出最佳截断值，字体缩放0.8倍
legacy.axes=T) # 使横轴从0到1，表示为1-特异度
ggroc1 <- ggroc(roc1,
legacy.axes = TRUE,
linetype = 2, size = 1, # 设置曲线线型和大小
colour = "#CC6666"); ggroc1
image.png
image.png
参考来源
近来发现一个学习统计的优秀网站、这个老师的代码超级简洁明了，让我们一起围观下。
