参考以下R语言代码：# Lab5: Cross-Validation and the Bootstrap # The Validation Set Approach install.packages("ISLR") library(ISLR) set.seed(1) train=sample(392,196) lm.fit=lm(mpg~horsepower,data=Auto,subset=train) attach(Auto) mean((mpg-predict(lm.fit,Auto))[-train]^2) lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train) mean((mpg-predict(lm.fit2,Auto))[-train]^2) lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train) mean((mpg-predict(lm.fit3,Auto))[-train]^2) set.seed(2) train=sample(392,196) lm.fit=lm(mpg~horsepower,subset=train) mean((mpg-predict(lm.fit,Auto))[-train]^2) lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train) mean((mpg-predict(lm.fit2,Auto))[-train]^2) lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train) mean((mpg-predict(lm.fit3,Auto))[-train]^2) # Leave-One-Out Cross-Validation glm.fit=glm(mpg~horsepower,data=Auto) coef(glm.fit) lm.fit=lm(mpg~horsepower,data=Auto) coef(lm.fit) library(boot) glm.fit=glm(mpg~horsepower,data=Auto) cv.err=cv.glm(Auto,glm.fit) cv.err$delta cv.error=rep(0,5) for (i in 1:5){ glm.fit=glm(mpg~poly(horsepower,i),data=Auto) cv.error[i]=cv.glm(Auto,glm.fit)$delta[1] } cv.error。按照以下要求根据参考代码，写出完整的R语言代码：要求： （1）50*30，30个变量 （2）原始模型为线性 （3）给出三组不同的原始模型系数 （4）计算出CV值 （5）画出CV error图和Prediction error图 （6）基于一倍标准差准则给出参数值上限

根据要求，完整的R语言代码如下：

# 50*30, 30个变量
set.seed(1)
X = matrix(rnorm(1500), nrow = 50, ncol = 30)
Y = rnorm(50)

# 原始模型为线性，给出三组不同的原始模型系数
lm.fit1 = lm(Y ~ X[,1])
lm.fit2 = lm(Y ~ X[,1:15])
lm.fit3 = lm(Y ~ X)

# 计算CV值
library(boot)
cv.error1 = cv.glm(data.frame(Y, X[,1]), lm.fit1)$delta[1]
cv.error2 = cv.glm(data.frame(Y, X[,1:15]), lm.fit2)$delta[1]
cv.error3 = cv.glm(data.frame(Y, X), lm.fit3)$delta[1]

# 画出CV error图和Prediction error图
par(mfrow=c(1,2))
plot(1:3, c(cv.error1, cv.error2, cv.error3), type = "b", xlab = "Model Complexity", ylab = "CV Error")
pred.error1 = sqrt(mean((Y - predict(lm.fit1, data.frame(X[,1])))^2))
pred.error2 = sqrt(mean((Y - predict(lm.fit2, data.frame(X[,1:15])))^2))
pred.error3 = sqrt(mean((Y - predict(lm.fit3, data.frame(X))))^2)
plot(1:3, c(pred.error1, pred.error2, pred.error3), type = "b", xlab = "Model Complexity", ylab = "Prediction Error")

# 基于一倍标准差准则给出参数值上限
upper.bound = mean(Y) + sd(Y)

需要注意的是，这里的CV值和Prediction error值都是在训练集上计算的。如果要对模型进行评估，需要在测试集上计算这些错误指标。