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Is there an easy way to include all possible two-way interactions in a model in R?
Given this model:
lm(a~b+c+d)
What syntax would be used so that the model would include b, c, d, bc, bd, and cd as explanatory variables, were bc is the interaction term of main effects b and c.
951
A two step process can be followed to create an interaction variable in R. First, the input variables must be centered to mitigate multicollinearity. Second, these variables must be multiplied to create the interaction variable.
First, it is possible to have multiple interaction terms in a model. Second, people may ask why you included some interaction terms and not others - you need to have a good answer.
To understand potential interaction effects, compare the lines from the interaction plot: If the lines are parallel, there is no interaction. If the lines are not parallel, there is an interaction.
You can write the following:
lm(a ~ (b + c + d)^2) This creates all combinations of two-way interactions between b, c, and d
For example:
lm(mpg ~ (cyl+disp+hp)^2, data = mtcars) gives:
Call: lm(formula = mpg ~ (cyl + disp + hp)^2, data = mtcars) Coefficients: (Intercept) cyl disp hp cyl:disp cyl:hp disp:hp 5.601e+01 -4.427e+00 -1.184e-01 -1.142e-01 1.439e-02 1.556e-02 -8.567e-05 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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