如何获得R中k重交叉验证的每个折叠的系数、z得分和p值？

library(caret)
library(glmnet)
library(boot)

data <- data.frame(X = ... , Y = ...)

fit_model <- function(data, indices) {
  train_data <- data[indices, ]
  test_data <- data[-indices, ]
  
  # 在训练集上拟合模型
  model <- glmnet(X, Y, data = train_data, alpha = 1)
  
  # 在测试集上进行预测
  predictions <- predict(model, newx = test_data$X)
  
  # 计算系数、z得分和p值
  coefs <- coef(model)
  z_scores <- coefs[, "z value"]
  p_values <- 2 * (1 - pnorm(abs(z_scores)))
  
  return(list(coefs = coefs, z_scores = z_scores, p_values = p_values))
}

k <- 10  # 设置交叉验证的折叠数
result <- cv.glmnet(X, Y, data = data, alpha = 1, nfolds = k, type.measure = "mse", keep = TRUE, parallel = TRUE)

fold_results <- result$byfold
for (i in 1:k) {
  fold <- fold_results[[i]]
  coefs <- fold$glmnet.fit$beta
  z_scores <- fold$glmnet.fit$beta / fold$glmnet.fit$se
  p_values <- 2 * (1 - pnorm(abs(z_scores)))
  
  # 打印每个折叠的系数、z得分和p值
  print(paste("Fold", i))
  print(coefs)
  print(z_scores)
  print(p_values)
}