在运行以下R代码时：library(glmnet) library(ggplot2) # 生成5030的随机数据和30个变量 set.seed(1111) n <- 50 p <- 30 X <- matrix(runif(n * p), n, p) y <- rnorm(n) # 生成三组不同系数的线性模型 beta1 <- c(rep(1, 3), rep(0, p - 3)) beta2 <- c(rep(0, 10), rep(1, 3), rep(0, p - 13)) beta3 <- c(rep(0, 20), rep(1, 3), rep(0, p - 23)) y1 <- X %% beta1 + rnorm(n) y2 <- X %% beta2 + rnorm(n) y3 <- X %% beta3 + rnorm(n) # 设置交叉验证折数 k <- 10 # 设置不同的lambda值 lambda_seq <- 10^seq(10, -2, length.out = 100) # 执行交叉验证和岭回归，并记录CV error和Prediction error cv_error <- list() pred_error <- list() for (i in 1:3) { # 交叉验证 cvfit <- cv.glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq, nfolds = k) cv_error[[i]] <- cvfit$cvm # 岭回归 fit <- glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq) pred_error[[i]] <- apply(X, 2, function(x) { x_mat <- matrix(x, nrow = n, ncol = p, byrow = TRUE) pred <- predict(fit, newx = x_mat) pred <- t(pred) # 转置 mean((x_mat %% fit$beta - switch(i, y1, y2, y3))^2, na.rm = TRUE) # 修改此处 }) } # 绘制图形 par(mfrow = c(3, 2), mar = c(4, 4, 2, 1), oma = c(0, 0, 2, 0)) for (i in 1:3) { # CV error plot plot(log10(lambda_seq), cv_error[[i]], type = "l", xlab = expression(log10), ylab = "CV error", main = paste0("Model ", i)) abline(v = log10(cvfit$lambda.min), col = "red") # Prediction error plot plot(log10(lambda_seq), pred_error[[i]], type = "l", xlab = expression(log10), ylab = "Prediction error", main = paste0("Model ", i)) abline(v = log10(lambda_seq[which.min(pred_error[[i]])]), col = "red") }。发生了以下错误：Error in xy.coords(x, y, xlabel, ylabel, log) : 'x'和'y'的长度不一样。请对原代码进行修正

这个错误是因为在绘制图形时，有些模型的CV error和Prediction error的长度不同，导致无法对齐。可以尝试以下修改：

1. 在执行岭回归时，修改计算Prediction error的代码，将修改此处的语句：

mean((x_mat %% fit$beta - switch(i, y1, y2, y3))^2, na.rm = TRUE)

为：

mean((x_mat %*% fit$beta - switch(i, y1, y2, y3))^2, na.rm = TRUE)

2. 在绘制图形时，将两个plot语句改为一起执行，并添加tryCatch语句，用于捕获出错的情况。具体修改如下：

par(mfrow = c(3, 2), mar = c(4, 4, 2, 1), oma = c(0, 0, 2, 0))
for (i in 1:3) {
  tryCatch({
    # CV error plot
    plot(log10(lambda_seq), cv_error[[i]], type = "l", xlab = expression(log10), ylab = "CV error", main = paste0("Model ", i))
    abline(v = log10(cvfit$lambda.min), col = "red")

    # Prediction error plot
    lines(log10(lambda_seq), pred_error[[i]], col = "blue")
    abline(v = log10(lambda_seq[which.min(pred_error[[i]])]), col = "red")
  }, error = function(e) {
    # 如果出现错误，打印错误信息
    cat("Error occurred while plotting Model ", i, ": ", conditionMessage(e), "\n")
  })
}