The statistical analysis of fluctuating asymmetry: REML estimation of a mixed regression model
Article first published online: 25 DEC 2001
Journal of Evolutionary Biology
Volume 12, Issue 1, pages 94–102, January 1999
How to Cite
Dongen, S. V., Molenberghs and Matthysen (1999), The statistical analysis of fluctuating asymmetry: REML estimation of a mixed regression model. Journal of Evolutionary Biology, 12: 94–102. doi: 10.1046/j.1420-9101.1999.00012.x
- Issue published online: 28 JUN 2008
- Article first published online: 25 DEC 2001
- directional asymmetry;
- fluctuating asymmetry;
- measurement error;
- mixed model;
The unbiased estimation of fluctuating asymmetry (FA) requires independent repeated measurements on both sides. The statistical analysis of such data is currently performed by a two-way mixed ANOVA analysis. Although this approach produces unbiased estimates of FA, many studies do not utilize this method. This may be attributed in part to the fact that the complete analysis of FA is very cumbersome and cannot be performed automatically with standard statistical software. Therefore, further elaboration of the statistical tools to analyse FA should focus on the usefulness of the method, in order for the correct statistical approaches to be applied more regularly.
In this paper we propose a mixed regression model with restricted maximum likelihood (REML) parameter estimation to model FA. This routine yields exactly the same estimates of FA as the two-way mixed ANOVA. Yet the advantages of this approach are that it allows (a) testing the statistical significance of FA, (b) modelling and testing heterogeneity in both FA and measurement error (ME) among samples, (c) testing for nonzero directional asymmetry and (d) obtaining unbiased estimates of individual FA levels. The switch from a mixed two-way ANOVA to a mixed regression model was made to avoid overparametrization.
Two simulation studies are presented. The first shows that a previously proposed method to test the significance of FA is incorrect, contrary to our mixed regression approach. In the second simulation study we show that a traditionally applied measure of individual FA [abs(left – right)] is biased by ME. The proposed mixed regression method, however, produces unbiased estimates of individual FA after modelling heterogeneity in ME. The applicability of this method is illustrated with two analyses.