"Donald F. Burrill" wrote:
> Inicidentally, I'd strongly recommend constructing interaction variables
> that are orthogonal at least to their own main effects (and lower-order
> interactions, when there are any), and possibly orthogonal to some or all
> of the apparently irrelevant other predictors. Else correlations between
> the interaction variables and other variables can, sometimes, be horribly
> confusing; especially with the "quantitative" (non-categorical)
> variables, whose products with other such variables are likely to be
> strongly (positively) correlated with the original variables merely
> because the original variables tend to be always positive and sometimes
> far from zero -- thus inducing what I've elsewhere called "spurious
> multicollinearity".
This I do not understand. I don't see the point in testing main effects
in the presence of interaction effects (unlike the pooled main effect +
interaction
effect tests which are completely invariant to coding). So I don't see
why coding matters. -Frank Harrell
