Abstract
Biodistance analysis can elucidate various aspects of past population structure. The most commonly adopted measure of divergence when estimating biodistances is the mean measure of divergence (MMD). The MMD is an unbiased estimator of population divergence but this property is lost when the dataset includes variables with very high or low frequency. In the present paper, we examine new measures of divergence based on untransformed binary data and the logit and probit transformations. It is shown that a measure of divergence based on untransformed data is a better unbiased estimator of population divergence. The conventional MMD is a satisfactory distance measure for binary data; however, it may produce biased estimations of population divergence when there are many traits with frequencies lower than 0.1 or/and greater than 0.9. Finally, the measures of divergence based on the probit and logit transformations are usually biased estimators.
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Acknowledgements
EN is grateful to all excavators who kindly provided access to the archeological material included in this paper. We would also like to thank two anonymous reviewers for their comments.
Funding
EN’s contribution to this research was supported by the H2020 Promised project (Grant Agreement 811068), and the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation (People in Motion project: EXCELLENCE/1216/0023).
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Supplementary Information
Supporting Data S1
Results for data transformations (XLSX 530 kb)
Supporting Data S2
Results from simulated data (XLSX 1300 kb)
Supporting Data S3
Data and results from dental nonmetric traits of archeological assemblages (XLSX 199 kb)
Supporting Data S4
Instructions and code of R functions (PDF 307 kb)
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Nikita, E., Nikitas, P. Measures of divergence for binary data used in biodistance studies. Archaeol Anthropol Sci 13, 40 (2021). https://doi.org/10.1007/s12520-021-01292-6
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DOI: https://doi.org/10.1007/s12520-021-01292-6