请对以下R代码进行优化，画图使用ggplot2：set.seed(123) data <- matrix(rnorm(50*30), nrow = 50, ncol = 30) library(glmnet) x <- data[,1:29] y <- data[,30] fit1 <- cv.glmnet(x, y, alpha = 1, nfolds = 10) fit2 <- cv.glmnet(x, y+rnorm(50), alpha = 1, nfolds = 10) fit3 <- cv.glmnet(x, y+rnorm(50,mean=2), alpha = 1, nfolds = 10) cv1 <- min(fit1$cvm) cv2 <- min(fit2$cvm) cv3 <- min(fit3$cvm) par(mfrow=c(3,2)) plot(fit1$lambda, fit1$cvm, type="l", xlab="lambda", ylab="CV error", main="Model 1") abline(v=fit1$lambda.min, col="red") plot(fit1$lambda, fit1$glmnet.fit$dev.ratio, type="l", xlab="lambda", ylab="Prediction error", main="Model 1") abline(v=fit1$lambda.min, col="red") plot(fit2$lambda, fit2$cvm, type="l", xlab="lambda", ylab="CV error", main="Model 2") abline(v=fit2$lambda.min, col="red") plot(fit2$lambda, fit2$glmnet.fit$dev.ratio, type="l", xlab="lambda", ylab="Prediction error", main="Model 2") abline(v=fit2$lambda.min, col="red") plot(fit3$lambda, fit3$cvm, type="l", xlab="lambda", ylab="CV error", main="Model 3") abline(v=fit3$lambda.min, col="red") plot(fit3$lambda, fit3$glmnet.fit$dev.ratio, type="l", xlab="lambda", ylab="Prediction error", main="Model 3") abline(v=fit3$lambda.min, col="red") cat("CV of Model 1: ", cv1, "\n") cat("CV of Model 2: ", cv2, "\n") cat("CV of Model 3: ", cv3, "\n")

可以使用 ggplot2 包来画图，同时可以使用 apply() 函数来避免重复的代码。以下是优化后的代码：

set.seed(123) 
data <- matrix(rnorm(50*30), nrow = 50, ncol = 30) 
library(glmnet) 
x <- data[,1:29] 
y <- data[,30] 
fit1 <- cv.glmnet(x, y, alpha = 1, nfolds = 10) 
fit2 <- cv.glmnet(x, y+rnorm(50), alpha = 1, nfolds = 10) 
fit3 <- cv.glmnet(x, y+rnorm(50,mean=2), alpha = 1, nfolds = 10) 
cv1 <- min(fit1$cvm) 
cv2 <- min(fit2$cvm) 
cv3 <- min(fit3$cvm) 

library(ggplot2)

# define a function to plot CV and Prediction errors
plot_errors <- function(fit, model){
  p1 <- ggplot() +
    geom_line(aes(x = fit$lambda, y = fit$cvm), color = "blue") +
    geom_vline(xintercept = fit$lambda.min, color = "red") +
    xlab("lambda") +
    ylab("CV error") +
    ggtitle(paste0("Model ", model))
  
  p2 <- ggplot() +
    geom_line(aes(x = fit$lambda, y = fit$glmnet.fit$dev.ratio), color = "blue") +
    geom_vline(xintercept = fit$lambda.min, color = "red") +
    xlab("lambda") +
    ylab("Prediction error") +
    ggtitle(paste0("Model ", model))
  
  plot_grid(p1, p2, ncol = 2)
}

# plot the errors for each model
plot_list <- lapply(list(fit1, fit2, fit3), function(fit) plot_errors(fit, which(list(fit1, fit2, fit3) == fit)))

# print the CV errors
cat("CV of Model 1: ", cv1, "\n")
cat("CV of Model 2: ", cv2, "\n")
cat("CV of Model 3: ", cv3, "\n")

# arrange and print the plots
plot_grid(plotlist = plot_list, ncol = 2)

这段代码首先定义了一个 `plot_errors()` 函数，用于绘制 CV error 和 Prediction error 的图形。然后，使用 `lapply()` 函数和一个列表，循环调用该函数来绘制每个模型的图形。最后，使用 `plot_grid()` 函数将所有的图形整合在一起。