Missing data in craniometrics: a simulation study |
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Authors: | Olivier Gauthier Pierre-Alexandre Landry François-Joseph Lapointe |
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Institution: | (1) Department of Biological Sciences, Mesa State College, Grand Junction, Colorado, 81502, USA;(2) Center for Infectious Diseases and Immunity, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA;(3) School of Biological Sciences, University of Northern Colorado, Greeley, CO 80639, USA |
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Abstract: | Craniometric measurements represent a useful tool for studying the differentiation of mammal populations. However, the fragility
of skulls often leads to incomplete data matrices. Damaged specimens or incomplete sets of measurements are usually discarded
prior to statistical analysis. We assessed the performance of two strategies that avoid elimination of observations: (1) pairwise
deletion of missing cells, and (2) estimation of missing data using available measurements. The effect of these distinct approaches
on the computation of inter-individual distances and population differentiation analyses were evaluated using craniometric
measurements obtained from insular populations of deer micePeromyscus maniculatus (Wagner, 1845). In our simulations, Euclidean distances were greatly altered by pairwise deletion, whereas Gower’s distance
coefficient corrected for missing data provided accurate results. Among the different estimation methods compared in this
paper, the regression-based approximations weighted by coefficients of determination (r
2) outperformed the competing approaches. We further show that incomplete sets of craniometric measurements can be used to
compute distance matrices, provided that an appropriate coefficient is selected. However, the application of estimation procedures
provides a flexible approach that allows researchers to analyse incomplete data sets. |
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