加载并显示示例数据

data(mtcars)
head(mtcars)#查看前5行，前10列
summary(mtcars)#按列统计

初步可视化

library(PerformanceAnalytics)#加载包
chart.Correlation(mtcars, histogram=TRUE, pch=19)

1、 线性拟合

lm1 <- lm(mpg~disp, data = mtcars)
su1 = summary(lm1)

初步可视化

library('ggpmisc')
f1 <- y ~ x #定义回归方程

library('ggplot2')
p1 <- ggplot(mtcars, aes(disp, mpg)) + theme_bw()+
  geom_point(show.legend = FALSE,color = 'blue',size = 5) +
  ggtitle('nonlinear fit')+
  geom_smooth(method = c('loess'), se=FALSE, col = 'black', span=0.5) + ##不规则拟合
  geom_smooth(method = "lm", formula = y ~ poly(x,2),color='blue',size = 2,se = T)+  ##二项式拟合
  stat_poly_eq(formula = f1,
               aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~")),
               parse = TRUE,label.x.npc = "right", label.y.npc = "top",size = 3) +#添加注释文字
  theme(axis.title= element_text(color = 'black',size= 12,face = 'bold'),
        axis.text = element_text(color = 'black',size = 10,face = 'bold'),
        plot.title = element_text(hjust = 0.5,size = 20,color = 'blue'))

输出图片

ggsave(p1,filename = 'p1.jpg',width = 4,height = 4,dpi = 600)

---

2、多项式拟合

lm2 = lm(mpg~disp + I(disp^2),data = mtcars)
su2 = summary(lm2)

初步可视化

f2 <- y ~ x + I(x^2) #定义回归方程

p2 <- ggplot(mtcars, aes(disp, mpg)) + theme_bw()+
  geom_point(show.legend = FALSE,color = 'blue',size = 5) +
  ggtitle('nonlinear fit')+
  geom_smooth(method = c('loess'), se=FALSE, col = 'black', span=0.5) + ##不规则拟合
  geom_smooth(method = "lm", formula = y ~ poly(x,2),color='blue',size = 2,se = T)+  ##二次拟合
  stat_poly_eq(formula = f2,
               aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~")),
               parse = TRUE,label.x.npc = "right", label.y.npc = "top",size = 3) +#添加注释文字
  theme(axis.title= element_text(color = 'black',size= 12,face = 'bold'),
        axis.text = element_text(color = 'black',size = 10,face = 'bold'),
        plot.title = element_text(hjust = 0.5,size = 20,color = 'blue'))

保存图片

ggsave(p2,filename = 'p2.jpg',width = 4,height = 4,dpi = 600)