如何评估R中方程的误差/不确定度？

# 假设我们有一个简单的线性回归模型
model <- lm(y ~ x, data = mydata)

# 计算均方误差
mse <- mean((mydata$y - predict(model))^2)
print(paste("Mean Squared Error:", mse))

# 计算决定系数（R^2）
r_squared <- summary(model)$r.squared
print(paste("R-squared:", r_squared))

# 检查残差的正态性
plot(model, which = 2) # Q-Q plot

# 检查残差的方差齐性
plot(model, which = 1) # Residuals vs Fitted plot

library(caret)

# 设置交叉验证参数
train_control <- trainControl(method="cv", number=10)

# 训练模型
model_cv <- train(y ~ x, data = mydata, method = "lm", trControl = train_control)

# 输出交叉验证结果
print(model_cv$results)