Morphometrics with RSpringer Science & Business Media, 15 déc. 2008 - 317 pages 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. |
Table des matières
| 1 | |
Acquiring and Manipulating Morphometric Data | 25 |
Traditional Statistics for Morphometrics 69 | 68 |
Modern Morphometrics Based on Configurations of Landmarks | 133 |
Statistical Analysis of Outlines | 205 |
Outlines | 213 |
Statistical Analysis of Shape using Modern Morphometrics | 233 |
Going Further with R 281 | 280 |
Functions Developed in this Text | 299 |
Autres éditions - Tout afficher
Expressions et termes fréquents
3D data aligned allometry analyzing ANOVA argument array axes baseline Bookstein calculate centered preshape centroid clustering columns compute configuration matrix coordinates correlation corresponds covariance curve dataset defined deformation grids degrees of freedom developed digitized display distribution eigenvectors ellipse elliptic Fourier equally spaced estimate Euclidean distance Euclidean distance matrix example factor fluctuating asymmetry Fourier analysis gorilla graph graphical groups image files individual interlandmark distances Iris setosa isometry linear discriminant Mahalanobis distance major axis Mantel test mean shape measurement error median methods morphological morphometrics multivariate nonaffine number of landmarks object observations obtain outline package parameters perform pixel plot points Procrustes analysis Procrustes distance Procrustes superimposition pseudolandmarks regression relationship Required functions residual resistant-fit rotation rotation matrix sample scale shape change shape space shape variation simulated singular-value decomposition species statistical sum of squares superimposed configurations tion transformation triangle values variables variance variance-covariance matrix vector Π Π Π
