含交互项的单一模型
R 中级回归
Richie Cotton
Data Evangelist at DataCamp
什么是交互作用？
在鱼类数据集中
长度对期望质量的影响因物种而异。
更一般地说
一个解释变量对期望响应的影响会随另一个解释变量的取值而变化。
指定交互项
无交互项
response ~ explntry1 + explntry2
有交互项（隐式）
response_var ~ explntry1 * explntry2
有交互项（显式）
response ~ explntry1 + explntry2 + explntry1:explntry2
无交互项
mass_g ~ length_cm + species
有交互项（隐式）
mass_g ~ length_cm * species
有交互项（显式）
mass_g ~ length_cm + species + length_cm:species
运行模型
lm(mass_g ~ length_cm * species, data = fish)
Call:
lm(formula = mass_g ~ length_cm * species, data = fish)
Coefficients:
(Intercept) length_cm speciesPerch speciesPike
-1035.348 54.550 416.172 -505.477
speciesRoach length_cm:speciesPerch length_cm:speciesPike length_cm:speciesRoach
705.971 -15.639 -1.355 -31.231
更易理解的系数
mdl_inter <- lm(mass_g ~ species + species:length_cm + 0, data = fish)
Call:
lm(formula = mass_g ~ species + species:length_cm + 0, data = fish)
Coefficients:
speciesBream speciesPerch speciesPike speciesRoach
-1035.35 -619.18 -1540.82 -329.38
speciesBream:length_cm speciesPerch:length_cm speciesPike:length_cm speciesRoach:length_cm
54.55 38.91 53.19 23.32
熟悉的数字
speciesBream speciesPerch speciesPike speciesRoach
-1035.35 -619.18 -1540.82 -329.38
speciesBream:length_cm speciesPerch:length_cm speciesPike:length_cm speciesRoach:length_cm
54.55 38.91 53.19 23.32
coefficients(mdl_bream)
(Intercept) length_cm
-1035.34757 54.54998
Passons à la pratique !
R 中级回归
