This book aims to explain how to use R to perform morphometrics. Morpho- tric analysis is the study of shape and size variations and covariations and their covariations with other variables. Morphometrics is thus deeply rooted within stat- tical sciences. While most applications concern biology, morphometrics is becoming common tools used in archeological, palaeontological, geographical, or medicine disciplines. Since the recent formalizations of some of the ideas of predecessors, such as D’arcy Thompson, and thanks to the development of computer techno- gies and new ways for appraising shape changes and variation, morphometrics have undergone, and are still undergoing, a revolution. Most techniques dealing with s- tistical shape analysis have been developed in the last three decades, and the number of publications using morphometrics is increasing rapidly. However, the majority of these methods cannot be implemented in available software and therefore prosp- tive students often need to acquire detailed knowledge in informatics and statistics before applying them to their data. With acceleration in the accumulation of me- ods accompanying the emerging science of statistical shape analysis, it is becoming important to use tools that allow some autonomy. R easily helps ful?ll this need. Risalanguage andenvironment forstatisticalcomputingandgraphics. Although there is an increasing number of computer applications that perform morphometrics, using R has several advantages that confer to users considerable power and possible new horizons in a world that requires rapid adaptability.

Morphometrics has undergone a revolutionary transformation in the past two decades as new methods have been developed to address shortcomings in the traditional multivirate analysis of linear distances, angles, and indices. While there is much active research in the field, the new approaches to shape analysis are already making significant and ever-increasing contributions to biological research, including physical anthropology. Modern Morphometrics in Physical Anthropology highlights the basic machinery of the most important methods, while introducing novel extensions to these methods and illustrating how they provide enhanced results compared to more traditional approaches. Modern Morphometrics in Physical Anthropology provides a comprehensive sampling of the applications of modern, sophisticated methods of shape analysis in anthropology, and serves as a starting point for the exploration of these practices by students and researchers who might otherwise lack the local expertise or training to get started. This text is an important resource for the general morphometric community that includes ecologists, evolutionary biologists, systematists, and medical researchers.

The historical background to morphometric studies; The mathematical framework of morphometric analyses; The biological background to multivariate analysis; Some simple forerunners of multivariate morphometrics; Some practical considerations; The use of angles between vectors; Formal background of discriminant functions analysis; Canonical variates; Canonical correlations; Variation in the apparently homogeneous sample; Du praw's non-linnean taxonomy; Principal components and principal coordinates analyses; Factor analysis; The measurement of environmental variation; Multivariate studies in ecology; Phytosociological studies; The quantitative comparison of faunal elements; Genetical aspects of morphometrics; Coordinated applications of multivariate analysis; Some applications of discriminatory topology; The relationships between morphometric analyses and numerical taxonomy...

The first edition of Geometric Morphometrics for Biologists has been the primary resource for teaching modern geometric methods of shape analysis to biologists who have a stronger background in biology than in multivariate statistics and matrix algebra. These geometric methods are appealing to biologists who approach the study of shape from a variety of perspectives, from clinical to evolutionary, because they incorporate the geometry of organisms throughout the data analysis. The second edition of this book retains the emphasis on accessible explanations, and the copious illustrations and examples of the first, updating the treatment of both theory and practice. The second edition represents the current state-of-the-art and adds new examples and summarizes recent literature, as well as provides an overview of new software and step-by-step guidance through details of carrying out the analyses. - Contains updated coverage of methods, especially for sampling complex curves and 3D forms and a new chapter on applications of geometric morphometrics to forensics - Offers a reorganization of chapters to streamline learning basic concepts - Presents detailed instructions for conducting analyses with freely available, easy to use software - Provides numerous illustrations, including graphical presentations of important theoretical concepts and demonstrations of alternative approaches to presenting results

This first systematic survey of the subject combines multivariate statistical analysis, geometry, and biomathematics.

The high-level language of R is recognized as one of the mostpowerful and flexible statistical software environments, and israpidly becoming the standard setting for quantitative analysis,statistics and graphics. R provides free access to unrivalledcoverage and cutting-edge applications, enabling the user to applynumerous statistical methods ranging from simple regression to timeseries or multivariate analysis. Building on the success of the author’s bestsellingStatistics: An Introduction using R, The R Book ispacked with worked examples, providing an all inclusive guide to R,ideal for novice and more accomplished users alike. The bookassumes no background in statistics or computing and introduces theadvantages of the R environment, detailing its applications in awide range of disciplines. Provides the first comprehensive reference manual for the Rlanguage, including practical guidance and full coverage of thegraphics facilities. Introduces all the statistical models covered by R, beginningwith simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression andanalysis of variance, through to generalized linear models,generalized mixed models, time series, spatial statistics,multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates andprofessionals in science, engineering and medicine. It is alsoideal for students and professionals in statistics, economics,geography and the social sciences.

A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while retaining sufficient detail for more specialist statisticians to appreciate the challenges and opportunities of this new field. Computer code has been included for instructional use, along with exercises to enable readers to implement the applications themselves in R and to follow the key ideas by hands-on analysis. Statistical Shape Analysis: with Applications in R will offer a valuable introduction to this fast-moving research area for statisticians and other applied scientists working in diverse areas, including archaeology, bioinformatics, biology, chemistry, computer science, medicine, morphometics and image analysis .

Morphometry of the Human Lung considers the developments in understanding the quantitative anatomy of the lung, and in the correlation of anatomy with physiology. This book is composed of 11 chapters, and begins with an overview of a systematic approach to a quantitative morphologic analysis of the architecture of the human lung, followed by a presentation of general problems of methodology and the derivation of reliable dimensional models of this organ. The subsequent chapters describe the methods of preparation of tissues, methods of random sampling, and adaptation of methodologies from other fields of science. These topics are followed by discussions the mathematical formulations for the translation of the data into the desired geometric forms and a technique of counting. The final chapters look into the mode of distribution and geometric forms that should eventually facilitate mathematical and physical considerations regarding the function of the lungs. These chapters also consider the application of these quantitative methods to the study of pathologic specimens, providing a most timely renovation of morphologic pathology. This book will be of value to pulmonologists, physiologists, and researchers who are interested in lung morphometry.

Statistical Shape and Deformation Analysis: Methods, Implementation and Applications contributes enormously to solving different problems in patient care and physical anthropology, ranging from improved automatic registration and segmentation in medical image computing to the study of genetics, evolution and comparative form in physical anthropology and biology. This book gives a clear description of the concepts, methods, algorithms and techniques developed over the last three decades that is followed by examples of their implementation using open source software. Applications of statistical shape and deformation analysis are given for a wide variety of fields, including biometry, anthropology, medical image analysis and clinical practice. - Presents an accessible introduction to the basic concepts, methods, algorithms and techniques in statistical shape and deformation analysis - Includes implementation examples using open source software - Covers real-life applications of statistical shape and deformation analysis methods

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.