Article thumbnail

A new model for visualizing interactions in analysis of variance

By P.J.F. Groenen and A.J. Koning


In analysis of variance, there is usually little attention for interpretingthe terms of the effects themselves, especially for interactioneffects. One of the reasons is that the number of interaction-effectterms increases rapidly with the number of predictor variables andthe number of categories. In this paper, we propose a new model,called the interaction decomposition model, that allows to visualizethe interactions. We argue that with the help of the visualization, theinteraction-effect terms are much easier to interpret. We apply ourmethod to predict holiday spending1 using seven categorical predictorvariables.

OAI identifier:

Suggested articles


  1. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques.
  2. (1992). Algorithmic approaches for ļ¬tting bilinear models.
  3. (1988). Correspondence analysis of multivariate categorical data by weighted least squares.
  4. (1996). Generalised bilinear regression.
  5. (1996). Generalized bilinear models.
  6. (1972). Generalized linear models.
  7. (1946). Mathematical methods of statistics.
  8. (1995). Multiplicative interaction in generalized linear models.
  9. (1990). Nonlinear multivariate analysis.
  10. (1984). Theory and applications of correspondence analysis.
  11. (1997). Three-factor association models for three-way contingency tables.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.