Donald Burrill wrote: > (One could have constructed <d*g> to be orthogonal to both <d> and <g> > -- see my White Paper on interactions in MR, on the MINITAB web site > www.minitab.com for an example and description of how to do that -- so > that <d*g> represents what one might call the "pure interaction" effect, > uncontaminated with main effects;
Donald, if the only objective is *testing* an interaction, is there a good reason why not just compare the improvement in fit achieved by the model Z = aX+bY+c(X*Y) over Z=eX+fY? This gets you the uncontaminated improvement explainable solely with the interaction effect. Because we're looking only at the quality of fit, the correlations among predictors are irrelevant, and a good fitting procedure doesn't mind multicollinearity. But I agree that orthogonalization is important when you try to decompose the variance explained among the predictors. -- mag. Aleks Jakulin http://ai.fri.uni-lj.si/aleks/ Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
