Geometric Morphometrics for Biologists is an introductory textbook for a course on geometric morphometrics, written for graduate students and upper division undergraduates, covering both theory of shape analysis and methods of multivariate analysis. It is designed for students with minimal math bac
Geometric Morphometrics for Biologists
โ Scribed by Miriam L. Zelditch, Donald L. Swiderski, David H Sheets, William L. Fink
- Publisher
- Academic Press
- Year
- 2004
- Tongue
- English
- Leaves
- 432
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Geometric Morphometrics for Biologists is an introductory textbook for a course on geometric morphometrics, written for graduate students and upper division undergraduates, covering both theory of shape analysis and methods of multivariate analysis. It is designed for students with minimal math background; taking them from the process of data collection through basic and more advanced statistical analyses. Many examples are given, beginning with simple although realistic case-studies, through examples of complex analyses requiring several different kinds of methods. The book also includes URL's for free software and step-by-step instructions for using the software.
* Accessible, student-tested introduction to sophisticated methods of biological shape analysis
* Detailed instructions for conducting analyses with freely available, easy to use software
* Numerous illustrations; including graphical presentations of important theoretical concepts and demonstrations of alternative approaches to presenting results
* Many realistic examples, both simple and complex, from on-going research
* Comprehensive glossary of technical terms
โฆ Table of Contents
1.pdf......Page 1
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Morphometrics is the statistical study of biological shape and shape change. Its richest data are landmarks, points, such as the bridge of the nose, that have biological names as well as geometric locations. This book is the first systematic survey of morphometric methods for landmark data.