I have created a MLR model where my predictor variables are continuous and categorical. I am interested in the interactions between the categorical variables.

Let's say I have the response variable $y$, and three predictor variables $x_1$, which is continuous and $x_2$ and $x_3$, which are binary, 1 if it is in and 0 if it is not.

Before I create the interaction terms I do mean subtraction to avoid linear dependency. So $$\hat{x_2} = x_2-\text{mean}(x_2)$$ $$\hat{x_3} = x_3 - \text{mean}(x_3)$$

So my linear model is now: $$y = ax_1 + bx_2 + cx_3 +d(\hat{x_2}*\hat{x_3}).$$

My question is about creating a test set of data with the same variables. When creating the test set do I make the interaction term as: $$ x_2 * x_3 $$ without the mean subtraction?

Or do I also use mean subtraction when creating the test data iteraction term as: $$ \hat{x_2}*\hat{x_3} $$

  • $\begingroup$ What exactly are you hoping to accomplish by the "mean subtraction to avoid linear dependency"? That seems only to be causing unnecessary problems for you. Both main and interaction effects are much more easily interpreted if you keep the original 0/1 coding, and there won't be the data-set dependence that you note in your attempt to examine a new test set. $\endgroup$ – EdM Dec 5 '14 at 20:09
  • $\begingroup$ This is where the idea came from - r-bloggers.com/…. Now that I think about it, there more than likely will not be any linear dependency unless the x_2 and x_3 are equal. $\endgroup$ – RDizzl3 Dec 5 '14 at 20:11

Even in the example you cite in your comment, which involves continuous rather than categorical variables, it's not clear that the centering gains anything. In R, for your case you simply would write something like lm(y~ x1 + x2*x3) to get both main effects and the interaction for x2 and x3. The "*" here isn't an arithmetic multiplication, but rather an instruction to determine both main and interaction effects. In the default treatment contrasts used by R, the main effect for x2 will be the influence on y of changing x2 from 0 to 1 when the value of x3 is 0, and the interaction term will be the additional influence of x2 (positive or negative) when x3 is 1 instead.


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