R语言基于logitic回归的RCS曲线

在R语言中，RCS（Receiver Operating Characteristic, 收受者操作特性）曲线常用于评估逻辑回归模型的性能。RCS曲线展示了真阳性率（Sensitivity, TP / (TP + FN)，即召回率）与假阳性率（False Positive Rate, FP / (FP + TN），即1减特异性）之间的关系。以下是创建RCS曲线的基本步骤：

1. 安装必要的包[^1]:

install.packages("ggplot2") # 如果尚未安装
library(ggplot2)

2. 使用`glm()`函数执行逻辑回归:

# 假设你有一个名为data的数据集，包含响应变量response和预测变量predictor
model <- glm(response ~ predictor, data = data, family = "binomial")

3. 预测概率并转换为类标签:

prob <- predict(model, type = "response")
prediction <- ifelse(prob > 0.5, "positive", "negative")

4. 计算真正例(TP), 假正例(FP), 真反例(TN), 和假反例(FN):

confusion_matrix <- table(prediction, data$response)
TP <- confusion_matrix["positive", "positive"]
FP <- confusion_matrix["positive", "negative"]
TN <- confusion_matrix["negative", "negative"]
FN <- confusion_matrix["negative", "positive"]

5. 创建RCS曲线:

# 真阳性和假阳性的组合
thresholds <- seq(from = 0, to = 1, by = 0.01)
TP_rates <- TP / (TP + FN)
FP_rates <- FP / (FP + TN)

# 构建RCS数据框
RCS_data <- data.frame(Sensitivity = TP_rates, 
                       False_Positive_Rate = FP_rates,
                       Threshold = thresholds)

# 绘制RCS曲线
RCS_plot <- ggplot(RCS_data, aes(x = False_Positive_Rate, y = Sensitivity)) +
             geom_line(color = "blue") +
             labs(title = "Receiver Operating Characteristic Curve",
                  x = "False Positive Rate",
                  y = "True Positive Rate")
RCS_plot