WEEK4

两两线性回归

# 加载必要的库

library(ggplot2)

library(gridExtra)

# 读取CSV文件

df <- read.csv("C:/Users/11030/Desktop/HKtestno0.csv", header = TRUE)

# 创建输出文件夹的路径

output_folder <- "C:/Users/11030/Desktop/rall2"

if (!dir.exists(output_folder)) {

dir.create(output_folder)

}

# 获取所有的列名，假定它们都是数值型的列

column_names <- names(df)

# 对每对变量进行线性回归并绘图

for (i in 1:(length(column_names)-1)) {

for (j in (i+1):length(column_names)) {

# 拟合线性模型

model <- lm(reformulate(column_names[i], response = column_names[j]), data = df)

# 获取R平方值

r_squared <- summary(model)$r.squared

# 绘制散点图和拟合线

p <- ggplot(df, aes_string(x = column_names[i], y = column_names[j])) +

geom_point() +

geom_smooth(method = "lm", formula = y ~ x, color = "blue") +

theme_minimal() +

ggtitle(paste("Scatter plot of", column_names[j], "vs", column_names[i], "\nR-squared:", round(r_squared, 3)))

# 保存图像

file_name <- paste0(column_names[i], "_vs_", column_names[j], "_scatterplot.png")

ggsave(file.path(output_folder, file_name), plot = p, width = 5, height = 4, dpi = 300)

}

}

# 保存图像

file_name <- paste(column_names[i], "_vs_", column_names[j], ".png", sep = "")

file_path <- file.path(output_folder, file_name)

ggsave(file_path, plot = p, width = 6, height = 4)

}

}