请基于以下R代码：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)，每个线性模型组，均进行交叉验证，并以par(3,2)的规格分别画出每组的交叉验证误差图和预测误差图

以下是基于给定的R代码，每个线性模型组，均进行交叉验证，并以par(3,2)的规格分别画出每组的交叉验证误差图和预测误差图的完整R代码：

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)

# 线性回归中分别计算三组的CV值
cv1 <- cv.glmnet(X, y1, alpha = 0)
cv2 <- cv.glmnet(X, y2, alpha = 0)
cv3 <- cv.glmnet(X, y3, alpha = 0)

# 岭回归中计算三组的CV值并画图
ridge1 <- glmnet(X, y1, alpha = 0)
ridge2 <- glmnet(X, y2, alpha = 0)
ridge3 <- glmnet(X, y3, alpha = 0)

# 画图
par(mfrow = c(3, 2))

# 以交叉验证误差为纵坐标的图
plot(cv1, main = "CV Error for Model 1")
plot(cv2, main = "CV Error for Model 2")
plot(cv3, main = "CV Error for Model 3")

# 以预测误差为纵坐标的图
plot(ridge1, xvar = "lambda", main = "Prediction Error for Model 1")
plot(ridge2, xvar = "lambda", main = "Prediction Error for Model 2")
plot(ridge3, xvar = "lambda", main = "Prediction Error for Model 3")