如何在R中运行多重回归的montecarlo模拟？

install.packages(c("stats", "boot"))

library(stats)
library(boot)

set.seed(123)  # 设置随机种子以确保结果可重复
n <- 100  # 样本大小
x <- rnorm(n)  # 生成自变量x
y <- 2*x + rnorm(n)  # 生成因变量y（带有误差项）
data <- data.frame(x, y)  # 创建数据框

model <- lm(y ~ x, data = data)

montecarlo <- function(data, model, num_simulations) {
  coefs <- matrix(NA, nrow = num_simulations, ncol = length(coef(model)))  # 存储回归系数
  for (i in 1:num_simulations) {
    bootstrap_data <- data[sample(nrow(data), replace = TRUE), ]  # 有放回抽样
    bootstrap_model <- lm(y ~ x, data = bootstrap_data)  # 拟合回归模型
    coefs[i, ] <- coef(bootstrap_model)  # 存储回归系数
  }
  return(coefs)
}

num_simulations <- 1000  # 模拟次数
results <- montecarlo(data, model, num_simulations)  # 运行Monte Carlo模拟