Abstract #300672

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JSM 2003 Abstract #300672
Activity Number: 86
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #300672
Title: Rotating Principal Components to Increase Interpretability
Author(s): Steven M. LaLonde*+
Companies: Rochester Institute of Technology
Address: 98 Lomb Memorial Dr., Rochester, NY, 14623-5604,
Keywords: principal components ; factor rotation ; multivariate analysis ; factor analysis ; curves ; data reduction
Abstract:

The dependent variable in many experiments is actually a multivariate response vector. Some methods seek to retain the complete response vector, such as canonical correlation analysis, or partial least squares. Often however, "parameters" of these response vectors, or curves, are calculated, and the parameters are modeled using response surface methodology. The choice of how to calculate the parameters is usually based on the intuition of the experimenter. In the experience of the author, these parameters are often correlated, resulting in difficult interpretations of the univariate response surface models. This paper investigates the use of principal components for data reduction as a precursor to response surface modeling. However, instead of the usual varimax, or equimax rotations typically seen in these types of applications, the author proposes a new rotation criteria aimed on deriving orthogonal parameters that are best explained by the independent variables in the experiment.

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