如何在R中将两个X变量对同一Y变量的回归线绘入同一图

方法1：直接添加两条独立回归线 #

无需转换数据格式，通过两次调用geom_smooth()分别指定不同自变量，用颜色、线型区分两条线：

# 加载ggplot2包
library(ggplot2)

# 构造你的数据集
Data <- data.frame(
  Spending = c(100, 200, 1000),
  Score = c(2, 26, 90),
  Number = c(5, 89, 65)
)

# 绘制对比图
ggplot(Data, aes(y = Number)) +
  # 可选：添加原始散点，对应两个自变量的分布
  geom_point(aes(x = Spending), color = "#2E86AB") +
  geom_point(aes(x = Score), color = "#F24C4C") +
  # 绘制Number ~ Spending的回归线
  geom_smooth(aes(x = Spending), method = "lm", se = FALSE, color = "#2E86AB", linetype = "solid") +
  # 绘制Number ~ Score的回归线
  geom_smooth(aes(x = Score), method = "lm", se = FALSE, color = "#F24C4C", linetype = "dashed") +
  # 设置标签与标题
  labs(x = "自变量", y = "Number", title = "两条线性回归线斜率对比") +
  # 手动配置图例映射
  scale_color_manual(values = c("#2E86AB", "#F24C4C"), 
                     labels = c("Number ~ Spending", "Number ~ Score")) +
  scale_linetype_manual(values = c("solid", "dashed"), 
                        labels = c("Number ~ Spending", "Number ~ Score")) +
  theme_minimal()

方法2：转换为长格式数据（更贴合ggplot设计逻辑） #

先把宽格式数据转成长格式，将两个自变量合并为一列，通过分组一次性绘制两条回归线：

library(ggplot2)
library(dplyr)
library(tidyr)

# 构造数据集
Data <- data.frame(
  Spending = c(100, 200, 1000),
  Score = c(2, 26, 90),
  Number = c(5, 89, 65)
)

# 转换为长格式
Data_long <- Data %>%
  pivot_longer(cols = c(Spending, Score), 
               names_to = "预测变量", 
               values_to = "变量取值")

# 绘制对比图
ggplot(Data_long, aes(x = 变量取值, y = Number, color = 预测变量, linetype = 预测变量)) +
  geom_point(size = 2) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "两条线性回归线斜率对比", x = "自变量取值", y = "Number") +
  scale_color_brewer(palette = "Set1") +
  theme_minimal()

核心细节说明 #

• method = "lm" 指定使用线性模型拟合回归线
• se = FALSE 关闭置信区间（若需要展示，删除该参数即可）
• 用颜色、线型区分两个模型，直观对比斜率差异
• 第二种长格式方法更简洁，也便于后续扩展更多模型

内容的提问来源于stack exchange，提问作者Olivia