用R写一个stacking代码，基模型用RF、SVM、XGBoost，对训练集做5折交叉验证，模型精度评价指标用R2、RMSE和MAE

以下是一个基于RandomForest、SVM和XGBoost的stacking代码，使用R2、RMSE和MAE作为评价指标，对训练集进行5折交叉验证：

library(caret)
library(randomForest)
library(e1071)
library(xgboost)

# 读取数据
data <- read.csv("data.csv")

# 拆分数据集为训练集和测试集
set.seed(123)
trainIndex <- createDataPartition(data$target, p = .8, list = FALSE)
train <- data[trainIndex, ]
test <- data[-trainIndex, ]

# 定义基模型
rf_model <- randomForest(target ~ ., data = train)
svm_model <- svm(target ~ ., data = train)
xgb_model <- xgboost(data = as.matrix(train[,1:9]), label = train$target, nrounds = 100, verbose = FALSE)

# 交叉验证
folds <- createFolds(train$target, k = 5, list = TRUE)
numFolds <- length(folds)

# 定义stacking训练集和测试集
stack_train <- data.frame(matrix(NA, nrow = nrow(train), ncol = 3))
colnames(stack_train) <- c("RF", "SVM", "XGB")
stack_test <- data.frame(matrix(NA, nrow = nrow(test), ncol = 3))
colnames(stack_test) <- c("RF", "SVM", "XGB")

# 训练基模型
for (i in 1:numFolds) {
  trainIndex <- unlist(folds[i])
  cv_train <- train[trainIndex, ]
  cv_test <- train[-trainIndex, ]
  
  # 训练RF
  rf_pred <- predict(rf_model, cv_test)
  stack_train[-trainIndex, "RF"] <- rf_pred
  stack_test[, "RF"] <- stack_test[, "RF"] + predict(rf_model, newdata = test) / numFolds
  
  # 训练SVM
  svm_pred <- predict(svm_model, cv_test)
  stack_train[-trainIndex, "SVM"] <- svm_pred
  stack_test[, "SVM"] <- stack_test[, "SVM"] + predict(svm_model, newdata = test) / numFolds
  
  # 训练XGB
  xgb_pred <- predict(xgb_model, as.matrix(cv_test[,1:9]))
  stack_train[-trainIndex, "XGB"] <- xgb_pred
  stack_test[, "XGB"] <- stack_test[, "XGB"] + predict(xgb_model, as.matrix(test[,1:9])) / numFolds
}

# 计算stacking训练集上的R2、RMSE和MAE
stack_train$target <- train$target
stack_model <- lm(target ~ ., data = stack_train)
stack_train_pred <- predict(stack_model, stack_train)
stack_train_r2 <- R2(stack_train$target, stack_train_pred)
stack_train_rmse <- RMSE(stack_train$target, stack_train_pred)
stack_train_mae <- MAE(stack_train$target, stack_train_pred)

# 计算stacking测试集上的R2、RMSE和MAE
stack_test_pred <- predict(stack_model, stack_test)
stack_test_r2 <- R2(test$target, stack_test_pred)
stack_test_rmse <- RMSE(test$target, stack_test_pred)
stack_test_mae <- MAE(test$target, stack_test_pred)

# 打印结果
cat(paste0("Stacking Training R2: ", round(stack_train_r2, 4), "\n"))
cat(paste0("Stacking Training RMSE: ", round(stack_train_rmse, 4), "\n"))
cat(paste0("Stacking Training MAE: ", round(stack_train_mae, 4), "\n"))
cat(paste0("Stacking Testing R2: ", round(stack_test_r2, 4), "\n"))
cat(paste0("Stacking Testing RMSE: ", round(stack_test_rmse, 4), "\n"))
cat(paste0("Stacking Testing MAE: ", round(stack_test_mae, 4), "\n"))

注意：以上代码仅供参考，具体实现应根据数据集和模型的不同进行相应修改。