# 导包library(ggplot2)library(stats)# 创建数据集set.seed(233)x <- seq(0, 10, by = 0.2) #0-10步长为0.2y <- sin(x) + rnorm(length(x), mean = 0, sd = 0.2) #均值为0、标准差为0.2的正态分布data <- data.frame(x, y)# 设置局部加权回归带宽参数bandwidth <- 0.3# 执行局部加权回归loess_fit <- loess(y ~ x, data = data, span = bandwidth)# 输出回归方程summary(loess_fit)# 创建一个新数据集用于绘图new_data <- data.frame(x = seq(0, 10, by = 0.05))# 预测回归值predicted_values <- predict(loess_fit, newdata = new_data)# 创建图形ggplot(data, aes(x, y)) +geom_point() +               # 生成数据点geom_line(data = new_data, aes(x, predicted_values), color = "blue") + #生成线ggtitle("局部加权回归拟合") +xlab("X轴") +ylab("Y轴")# 计算均方误差mse <- mean((predicted_values - data$y)^2) #$y表示取data中y的值cat("均方误差：", mse, "\n")