单个模型评估

# install.packages("pROC")
library(pROC)calculate_metrics <- function(label, prediction) {# 加载所需的包library(pROC)# 计算ROC曲线和AUCroc_obj <- roc(label, prediction)auc_value <- auc(roc_obj)# 寻找最佳cutoffcutoff <- coords(roc_obj, "best", ret = "threshold")# 根据cutoff计算混淆矩阵predicted_label <- ifelse(prediction >= cutoff$threshold, 1, 0)confusion_matrix <- table(predicted_label, label)# 提取混淆矩阵中的TP，TN，FP和FNTP <- confusion_matrix[2, 2]TN <- confusion_matrix[1, 1]FP <- confusion_matrix[2, 1]FN <- confusion_matrix[1, 2]# 计算敏感度和特异度sensitivity <- TP / (TP + FN)specificity <- TN / (TN + FP)# 返回结果result <- list(AUC = auc_value,Cutoff = cutoff$threshold,Confusion_Matrix = confusion_matrix,TP = TP,TN = TN,FP = FP,FN = FN,Sensitivity = sensitivity,Specificity = specificity)return(result)
}# 使用示例数据调用函数
set.seed(123)
label <- sample(0:1, 100, replace = TRUE)
pred <- runif(100)
metrics <- calculate_metrics(label, pred)
print(metrics)

多个模型对比分析及统计检验

# install.packages("pROC")
library(pROC)
compare_models <- function(label, pred1, pred2) {# 加载所需的包library(pROC)# 计算ROC曲线roc1 <- roc(label, pred1)roc2 <- roc(label, pred2)# Delong检验delong_test_result <- roc.test(roc1, roc2, method = "delong")# 寻找最佳阈值cutoff1 <- coords(roc1, "best", ret = "threshold")cutoff2 <- coords(roc2, "best", ret = "threshold")# 计算敏感度和特异度predicted_label1 <- ifelse(pred1 >= cutoff1$threshold, 1, 0)predicted_label2 <- ifelse(pred2 >= cutoff2$threshold, 1, 0)sensitivity1 <- sum(predicted_label1 == 1 & label == 1) / sum(label == 1)sensitivity2 <- sum(predicted_label2 == 1 & label == 1) / sum(label == 1)specificity1 <- sum(predicted_label1 == 0 & label == 0) / sum(label == 0)specificity2 <- sum(predicted_label2 == 0 & label == 0) / sum(label == 0)# 比例检验sensitivity_test <- prop.test(x = c(sum(predicted_label1 == 1 & label == 1), sum(predicted_label2 == 1 & label == 1)),n = c(sum(label == 1), sum(label == 1)),correct = FALSE)specificity_test <- prop.test(x = c(sum(predicted_label1 == 0 & label == 0), sum(predicted_label2 == 0 & label == 0)),n = c(sum(label == 0), sum(label == 0)),correct = FALSE)# 返回结果result <- list(Delong_Test_Result = delong_test_result,Sensitivity_Test_Result = sensitivity_test,Specificity_Test_Result = specificity_test,Sensitivity1 = sensitivity1,Sensitivity2 = sensitivity2,Specificity1 = specificity1,Specificity2 = specificity2,Cutoff1 = cutoff1$threshold,Cutoff2 = cutoff2$threshold)return(result)
}# 使用示例数据调用函数
set.seed(123)
label <- sample(0:1, 100, replace = TRUE)
pred1 <- runif(100)
pred2 <- runif(100)comparison_result <- compare_models(label, pred1, pred2)
print(comparison_result)