数据data中Y为无序四分类，怎样用TPR、FPR绘制ROC曲线？请输出R代码。

下面是可以用于绘制ROC曲线的R代码：

# 利用预测分数和真实标签计算TPR和FPR
calculate_TPR_FPR <- function(predicted_scores, true_labels, threshold) {
  predicted_labels <- ifelse(predicted_scores >= threshold, 1, 0)
  TP <- sum(predicted_labels == 1 & true_labels == 1)
  FN <- sum(predicted_labels == 0 & true_labels == 1)
  FP <- sum(predicted_labels == 1 & true_labels == 0)
  TN <- sum(predicted_labels == 0 & true_labels == 0)
  tpr <- TP / (TP + FN)
  fpr <- FP / (FP + TN)
  return(c(tpr, fpr))
}

# 绘制ROC曲线
plot_ROC_curve <- function(scores, labels) {
  # 将标签转换为二进制矩阵
  label_matrix <- matrix(0, nrow = length(labels), ncol = 4)
  for (i in 1:length(labels)) {
    label_matrix[i, labels[i]] <- 1
  }

  # 计算每个类别下的TPR和FPR
  ROC_data <- list()
  for (i in 1:4) {
    # 提取预测分数和标签
    scores_i <- scores[, i]
    labels_i <- label_matrix[, i]

    # 计算TPR和FPR
    ROC_i <- data.frame(threshold = numeric(0), tpr = numeric(0), fpr = numeric(0))
    for (threshold in seq(from = 0, to = 1, by = 0.01)) {
      tpr_fpr <- calculate_TPR_FPR(predicted_scores = scores_i, true_labels = labels_i, threshold = threshold)
      ROC_i <- rbind(ROC_i, data.frame(threshold = threshold, tpr = tpr_fpr[1], fpr = tpr_fpr[2]))
    }

    # 将结果存储到列表中
    ROC_data[[i]] <- ROC_i
  }

  # 绘制ROC曲线
  plot(x = 0, y = 0, xlim = c(0, 1), ylim = c(0, 1), 
       xlab = "False Positive Rate", ylab = "True Positive Rate", 
       main = "ROC Curve")
  legend(x = "bottomright", inset = 0.05, 
         legend = c("Class 0", "Class 1", "Class 2", "Class 3"), 
         col = 1:4, lty = 1, cex = 0.8)
  for (i in 1:4) {
    ROC_i <- ROC_data[[i]]
    lines(x = ROC_i$fpr, y = ROC_i$tpr, col = i)
  }
}

# 示例数据
set.seed(123)
data <- data.frame(X1 = rnorm(100), X2 = rnorm(100), Y = sample(c(0, 1, 2, 3), 100, replace = TRUE))

# 示例用法
scores <- matrix(runif(400), nrow = 100, ncol = 4)
plot_ROC_curve(scores = scores, labels = data$Y)