[英]Difference between the interaction : and * term for formulas in StatsModels OLS regression
Hi I'm learning Statsmodel and can't figure out the difference between: and * (interaction terms) for formulas in StatsModels OLS regression.您好,我正在学习 Statsmodel,无法弄清楚 StatsModels OLS 回归中公式的:和 *(交互项)之间的区别。 Could you please give me a hint to figure this out?
你能给我一个提示来解决这个问题吗?
Thank you!谢谢!
The documentation: http://statsmodels.sourceforge.net/devel/example_formulas.html文档: http://statsmodels.sourceforge.net/devel/example_formulas.html
":" will give a regression without the level itself. “:”将给出没有关卡本身的回归。 just the interaction you have mentioned.
只是你提到的互动。
"*" will give a regression with the level itself + the interaction you have mentioned. “*” 将给予水平本身+你所提到的交互的回归。
for example 例如
a . a 。
GLMmodel = glm("y ~ a: b" , data = df)
you'll have only one independent variable which is the results of "a" multiply by "b" 你将只有一个自变量,它是“a”乘以“b”的结果
b . b 。
GLMmodel = glm("y ~ a * b" , data = df)
you'll have 3 independent variables which is the results of "a" multiply by "b" + "a" itself + "b" itself 你将有3个独立变量,这是“a”乘以“b”+“a”本身+“b”本身的结果
Using A*B
is really just shorthand for A + B + A:B
使用
A*B
实际上只是A + B + A:B
简写
A:B
specifies the interaction itself. A:B
指定交互本身。 This is literally the product of the two variables.这实际上是两个变量的乘积。 As such, it rarely makes sense to fit a model with only this term, so we almost always fit the main effects,
A
and B
too (see here for reasons why).因此,仅用这一项来拟合 model 几乎没有意义,因此我们几乎总是拟合主效应,
A
和B
(请参阅此处了解原因)。 Since this is so common, the shorthand notation A*B
for this is quite common in many statistical software packages/platforms.由于这很常见,因此缩写符号
A*B
在许多统计软件包/平台中很常见。
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