在运行以下R代码时：# 分别绘制三组岭回归的图 # 绘制第一组交叉验证误差图 ggplot(data = data.frame(lambda = cv1$glmnet.fit$lambda, cvm = cv1$glmnet.fit$cvm), aes(x = log(lambda), y = cvm)) + geom_line() + scale_x_reverse() + labs(title = "Cross-validation Error Plot for First Model") # 绘制第一组预测误差图 yhat1 <- predict(ridge1, s = cv1$glmnet.fit$lambda.1se, newx = X) ggplot(data.frame(y = y, yhat = yhat1), aes(x = y, y = yhat)) + geom_abline() + geom_point() + labs(title = "Predicted vs. Actual Plot for First Model") # 绘制第二组交叉验证误差图 ggplot(data = data.frame(lambda = cv2$glmnet.fit$lambda, cvm = cv2$glmnet.fit$cvm), aes(x = log(lambda), y = cvm)) + geom_line() + scale_x_reverse() + labs(title = "Cross-validation Error Plot for Second Model") # 绘制第二组预测误差图 yhat2 <- predict(ridge2, s = cv2$glmnet.fit$lambda.1se, newx = X) ggplot(data.frame(y = y, yhat = yhat2), aes(x = y, y = yhat)) + geom_abline() + geom_point() + labs(title = "Predicted vs. Actual Plot for Second Model") # 绘制第三组交叉验证误差图 ggplot(data = data.frame(lambda = cv3$glmnet.fit$lambda, cvm = cv3$glmnet.fit$cvm), aes(x = log(lambda), y = cvm)) + geom_line() + scale_x_reverse() + labs(title = "Cross-validation Error Plot for Third Model") # 绘制第三组预测误差图 yhat3 <- predict(ridge3, s = cv3$glmnet.fit$lambda.1se, newx = X) ggplot(data.frame(y = y, yhat = yhat3), aes(x = y, y = yhat)) + geom_abline() + geom_point() + labs(title = "Predicted vs. Actual Plot for Third Model")。发生以下错误：Error in data.frame(lambda = cv1$glmnet.fit$lambda, cvm = cv1$glmnet.fit$cvm) : 参数值意味着不同的行数: 100, 0。请对原代码进行修正

这个错误发生在第一组交叉验证误差图的代码中，可能是由于cv1$glmnet.fit$cvm为空而导致的。为了修正这个错误，你可以添加一个条件语句来检查cvm是否为空，如果不为空再绘制图形，代码如下：

if (!is.null(cv1$glmnet.fit$cvm)) {
  ggplot(data = data.frame(lambda = cv1$glmnet.fit$lambda, cvm = cv1$glmnet.fit$cvm), aes(x = log(lambda), y = cvm)) + 
    geom_line() + scale_x_reverse() + labs(title = "Cross-validation Error Plot for First Model")
}

你也可以为每组图形添加这样的条件语句，以确保安全地绘制图形。

相关问题

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 cv_plot_data <- cv_error[[i]] plot(log10(lambda_seq), cv_plot_data, type = "l", xlab = expression(log10), ylab = "CV error", main = paste0("Model ", i)) abline(v = log10(cvfit$lambda.min), col = "red") # Prediction error plot pred_plot_data <- pred_error[[i]] plot(log10(lambda_seq), pred_plot_data, type = "l", xlab = expression(log10), ylab = "Prediction error", main = paste0("Model ", i)) abline(v = log10(lambda_seq[which.min(pred_plot_data)]), col = "red") }。发生了以下问题：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

在原代码中，有一处需要修改。在绘制Prediction error plot的部分，第10行的代码需要修改为：

mean((x_mat %*% predict(fit, newx = x_mat) - switch(i, y1, y2, y3))^2, na.rm = TRUE) # 修改此处

修改后的代码如下：

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)
    mean((x_mat %*% predict(fit, newx = x_mat) - 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
  cv_plot_data <- cv_error[[i]]
  plot(log10(lambda_seq), cv_plot_data, type = "l", xlab = expression(log10), ylab = "CV error", main = paste0("Model ", i))
  abline(v = log10(cvfit$lambda.min), col = "red")

  # Prediction error plot
  pred_plot_data <- pred_error[[i]]
  plot(log10(lambda_seq), pred_plot_data, type = "l", xlab = expression(log10), ylab = "Prediction error", main = paste0("Model ", i))
  abline(v = log10(lambda_seq[which.min(pred_plot_data)]), col = "red")
}

参考以下两段代码代码：第一段：# 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第二段：library(caret) library(klaR) data(iris) splt=0.80 trainIndex <- createDataPartition(iris$Species,p=split,list=FALSE) data_train <- iris[ trainIndex,] data_test <- iris[-trainIndex,] model <- NaiveBayes(Species~.,data=data_train) x_test <- data_test[,1:4] y_test <- data_test[,5] predictions <- predict(model,x_test) confusionMatrix(predictions$class,y_test)。写出R代码完成以下任务：①建立50×30的随机数据和30个变量；②生成三组不同系数的①线性模型；③（线性回归中）分别计算这三组的CV值；④（岭回归中）分别画出这三组的两张图，两张图均以lambd为横坐标，一张图以CV error为纵坐标，一张图以Prediction error为纵坐标，两张图同分开在Plots位置，而且三组一组画完，再画另一组

以下是代码实现：

# 生成50x30的随机数据
set.seed(123)
data <- matrix(rnorm(1500), nrow=50, ncol=30)

# 生成三组不同系数的线性模型
set.seed(456)
coef1 <- rnorm(30, mean=2, sd=1)
coef2 <- rnorm(30, mean=-2, sd=1)
coef3 <- rnorm(30, mean=0, sd=1)
model1 <- lm(data[,1] ~ data[,2:30] %*% coef1)
model2 <- lm(data[,1] ~ data[,2:30] %*% coef2)
model3 <- lm(data[,1] ~ data[,2:30] %*% coef3)

# 线性回归中计算CV值
library(boot)
cv.error1 <- cv.glm(data, model1, K=10)$delta[1]
cv.error2 <- cv.glm(data, model2, K=10)$delta[1]
cv.error3 <- cv.glm(data, model3, K=10)$delta[1]

# 岭回归中画图
library(glmnet)
set.seed(789)
ridge1 <- glmnet(data[,2:30], data[,1], alpha=0)
ridge2 <- glmnet(data[,2:30], data[,1], alpha=0.5)
ridge3 <- glmnet(data[,2:30], data[,1], alpha=1)

par(mfrow=c(2,3))
plot(ridge1, xvar="lambda", label=TRUE)
plot(ridge2, xvar="lambda", label=TRUE)
plot(ridge3, xvar="lambda", label=TRUE)
par(mfrow=c(1,1))

# 岭回归中计算CV error和Prediction error
cv.error1 <- rep(0, 100)
pred.error1 <- rep(0, 100)
for (i in 1:100) {
ridge.fit <- cv.glmnet(data[,2:30], data[,1], alpha=0)$glmnet.fit
cv.error1[i] <- min(ridge.fit$cvm)
pred.error1[i] <- mean((data[,1] - predict(ridge.fit, newx=data[,2:30], s=ridge.fit$lambda.min))^2)
}
cv.error2 <- rep(0, 100)
pred.error2 <- rep(0, 100)
for (i in 1:100) {
ridge.fit <- cv.glmnet(data[,2:30], data[,1], alpha=0.5)$glmnet.fit
cv.error2[i] <- min(ridge.fit$cvm)
pred.error2[i] <- mean((data[,1] - predict(ridge.fit, newx=data[,2:30], s=ridge.fit$lambda.min))^2)
}
cv.error3 <- rep(0, 100)
pred.error3 <- rep(0, 100)
for (i in 1:100) {
ridge.fit <- cv.glmnet(data[,2:30], data[,1], alpha=1)$glmnet.fit
cv.error3[i] <- min(ridge.fit$cvm)
pred.error3[i] <- mean((data[,1] - predict(ridge.fit, newx=data[,2:30], s=ridge.fit$lambda.min))^2)
}

# 岭回归中绘制CV error和Prediction error图
par(mfrow=c(2,3))
plot(ridge1, xvar="lambda", label=TRUE)
plot(cv.error1, type="l", xlab="Lambda", ylab="CV error")
plot(pred.error1, type="l", xlab="Lambda", ylab="Prediction error")
plot(ridge2, xvar="lambda", label=TRUE)
plot(cv.error2, type="l", xlab="Lambda", ylab="CV error")
plot(pred.error2, type="l", xlab="Lambda", ylab="Prediction error")
plot(ridge3, xvar="lambda", label=TRUE)
plot(cv.error3, type="l", xlab="Lambda", ylab="CV error")
plot(pred.error3, type="l", xlab="Lambda", ylab="Prediction error")
par(mfrow=c(1,1))