兩個數值型解釋變數

R 的迴歸分析中級

Richie Cotton

Data Evangelist at DataCamp

視覺化 3 個數值變數

3D 散佈圖
2D 散佈圖，以反應變數著色

魚類資料集的另一個欄位

species	mass_g	length_cm	height_cm
Bream	1000	33.5	18.96
Bream	925	36.2	18.75
Roach	290	24.0	8.88
Roach	390	29.5	9.48
Perch	1100	39.0	12.80
Perch	1000	40.2	12.60
Pike	1250	52.0	10.69
Pike	1650	59.0	10.81

3D 散佈圖

library(plot3D)

scatter3D(fish$length_cm, fish$height_cm, fish$mass_g)

library(plot3D)
library(magrittr)

fish %$%
  scatter3D(length_cm, height_cm, mass_g)

3D 散佈圖

library(plot3D)
library(magrittr)

fish %$%
  scatter3D(length_cm, height_cm, mass_g)

3D 散佈圖：魚的長度、高度、重量

2D 散佈圖，以顏色表示反應

ggplot(
  fish, 
  aes(length_cm, height_cm, color = mass_g)
) +
  geom_point()

2D 散佈圖（以重量著色）：長度 × 高度

Viridis 色階

ggplot(
  fish, 
  aes(length_cm, height_cm, color = mass_g)
) +
  geom_point() +
  scale_color_viridis_c(option = "inferno")

Viridis 色階（inferno）散佈圖

含 2 個數值解釋變數的模型

mdl_mass_vs_both <- lm(mass_g ~ length_cm + height_cm, data = fish)

Call:
lm(formula = mass_g ~ length_cm + height_cm, data = fish)

Coefficients:
(Intercept)    length_cm    height_cm  
    -622.16        28.97        26.34

預測流程

explanatory_data <- expand_grid(
  length_cm = seq(5, 60, 5),
  height_cm = seq(2, 20, 2)
)

prediction_data <- explanatory_data %>% 
  mutate(
    mass_g = predict(mdl_mass_vs_both, explanatory_data)
  )

繪製預測結果

ggplot(
  fish, 
  aes(length_cm, height_cm, color = mass_g)
) +
  geom_point() +
  scale_color_viridis_c(option = "inferno") +
  geom_point(
    data = prediction_data, shape = 15, size = 3
  )

預測點疊圖（inferno 色階）

加入交互作用項

mdl_mass_vs_both_inter <- lm(mass_g ~ length_cm * height_cm, data = fish)

Call:
lm(formula = mass_g ~ length_cm * height_cm, data = fish)

Coefficients:
        (Intercept)            length_cm            height_cm  length_cm:height_cm  
           159.1144               0.3001             -78.1234               3.5455

預測流程（含交互作用）

explanatory_data <- expand_grid(
  length_cm = seq(5, 60, 5),
  height_cm = seq(2, 20, 2)
)

prediction_data <- explanatory_data %>% 
  mutate(
    mass_g = predict(mdl_mass_vs_both_inter, explanatory_data)
  )

繪製預測結果

ggplot(
  fish, 
  aes(length_cm, height_cm, color = mass_g)
) +
  geom_point() +
  scale_color_viridis_c(option = "inferno") +
  geom_point(
    data = prediction_data, shape = 15, size = 3
  )

預測點疊圖（含交互作用）

一起來練習吧！

R 的迴歸分析中級