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This workshop is designed to provide an introduction into the application and potential of geometric morphometric (GMM) methodologists for archaeologists, researchers and enthusiasts in the R Environment. The workshop first introduces participants to the mathematical underpinnings of statistical shape and form, before detailing the fundamentals of geometric morphometrics, emphasising its statistical power and converage in comparison to traditional morphometrics.
Day One (Morning):
• Introductory lecture (What is Geometric Morphometrics?)
• Some basics in R (environment and generating data)
• Organising data for morphometrics (image processing and writing data)
• Geometric Transformations
Day One (Afternoon):
• Procrustes Superimposition
• Other imposition methods (e.g. Resistant Fit)
• Representing shape differences (warp grids, arrows, etc.)
• Exploring shape variation and testing hypotheses (PCA, multivariate regression, MANOVA, LDA and cladistics)
Day Two (Morning):
• Acquiring outline data in R
• Fourier Analysis (principles)
• Radius Fourier Analysis
• Elliptic Fourier Analysis
• Outline data exploration
Day Two (Afternoon):
• Advanced analytical frameworks (e.g. Maximum Likelihoods and cluster techniques)
• Advanced outline methods (symmetry and rectilinearity extraction)
• Concluding remarks
Software: All data input, manipulation and analyses will be performed in the R Environment through geomorph (BETA version) and Momocs. Please ensure R/RStudio and all files are downloaded onto your computer/laptop before or at the beginning of the workshop. Other digitisation methods including tpsDig2 (https://life.bio.sunysb.edu/morph/soft-dataacq.html) and GUImorph (https://github.com/GUImorph/GUImorph) will also be showcased.
Adams, D.C. and Otárola-Castillo, E. (2013). Geomorph: an r package for the collection and analysis of geometric morphometric shape data. Methods of Ecology and Evolution 4, 393-399.
Adams, D.C., Rohlf, F.J. and Slice, D.E. (2004). Geometric morphometrics: ten years of progress following the ‘revolution’. Italian Journal of Zoology, 71, 5–16.
Bonhomme, V., Picq, S., Gaucherel, C., and Claude, J. (2014). Momocs: Outline analysis using R. Journal of Statistical Software, 56, 1–24.
Bookstein, F.L. (1991). Morphometric Tools for Landmark Data: Geometry and Biology. New York: Cambridge University Press.
Kovarovic, K., Aiello, L. C., Cardini, A. and Lockwood, C. A. (2011). Discriminant function analyses in archaeology: Are classification rates too good to be true? Journal of Archaeological Science, 38(11), 3006–3018.
MacLeod, N. (1999). Generalizing and extending the Eigenshape method of shape space visualization and analysis. Paleobiology, 25 (1), 107–138.
Slice, D.E. (2007). Geometric Morphometrics, Annual Review of Anthropology 36(1), 261–281.
Yoshioka, Y. (2004). Analysis of petal shape variation of Primula sieboldii by elliptic fourier descriptors and principal component analysis. Annals of Botany, 94(5), 657–664.
Zelditch, M.L., Swiderski D.L., Sheets H.D. and Fink, W.L. (2004). Geometric morphometrics for biologists: a primer. San Diego (CA): Elsevier Academic Press.