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Title:   Author  item
                                            TELLINGHUISEN, JOEL - Vanderbilt Universityitem
                                            Bolster, Carl
  Submitted to: Chemometrics and Intelligent Laboratory Systems
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 1/5/2011
                                    Publication  Date: 1/13/2011
                                    Citation: Tellinghuisen, J., Bolster, C.H. 2011. Using R^2 to compare least-squares fit models: When it must fail. Chemometrics and Intelligent Laboratory Systems. 105:220-222.Interpretive Summary: R2 is arguably the most frequently used, and abused, metric for judging goodness of fit. Indeed, R^2 is often used to select the best-fit model among competing models when using least-squares regression. In this study we show through the use of computer generated data how R^2 can be properly used when comparing different forms of the same model. Our results show that with proper weighting R^2 can be used correctly for model comparisons; however, R^2 comparisons then become equivalent to comparisons of the estimated fit variance s^2 in unweighted fitting, or of the reduced chi-square in weighted fitting with weights taken as inverse variances. The latter metrics are much more easily interpreted, and thus are better than R^2 for such purposes. When models are compared by fitting data that have been mathematically transformed in different ways, with proper weighting, s^2 and chi-square remain valid; but R2 fails miserably.
                                Technical Abstract: R^2 can be used correctly to select from among competing least-squares fit models when the data are fitted in common form and with common weighting. However, then R^2 comparisons become equivalent to comparisons of the estimated fit variance s^2 in unweighted fitting, or of the reduced chi-square in weighted fitting with weights taken as inverse variances. The latter metrics are arguably more easily interpreted, thus better than R^2 for such purposes. When models are compared by fitting data that have been mathematically transformed in different ways, with proper weighting, s^2 and chi-square remain valid; but R2 fails miserably.


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