1. An Introduction to R / Image Processing / Organizing Morphometric Data.
1.1. Some Basics in R.
1.1.1. The R Environment.
1.1.2. R objects, Assigning, Indexing.
1.1.3. Generating Data in R.
1.1.4. 2D and 3D Plots in R; Interacting with the Graphs.
1.2. Organizing Data for Morphometrics.
1.2.1. Data-frame, Array and List.
1.2.2. Converting and Coercing Objects.
1.2.3. Read and Write Morphometric Data in R.
1.3. Image Processing in R.
1.3.1. Reading Various Image Files.
1.3.2. Obtaining Image Properties.
1.3.3. Modifying Image Properties: Contrast, Channels, Saturation Directly from R or by Interfacing R with Imagemagick.
1.4. Simple Tests, Simple Linear Modelling, Alternatives to Linear Modelling, an example using traditional morphometrics.
1.4.1. Defining size and shape using PCA and log-shape ratio approaches.
1.4.2. Getting stats and test outputs.
1.4.3. Testing assumptions of linear modelling.
1.4.4. Testing for allometry and isometry.
1.4.5. Solutions when assumptions of linear modelling are not met.
2. Landmark data.
2.1. Acquiring Landmark Data in R.
2.2. Plotting Landmark Configurations in 2 and in 3D.
2.2.1. Using Different Symbols and Setting the Graphical Parameters.
2.2.2. Labeling Landmarks.
2.3. Geometric Transformation with Landmark Configurations.
2.3.1. Translation.
2.3.2. Scaling using Baseline or Centroid Size.
2.3.3. Rotation.
2.4. Superimposing and Comparing Two Shapes.
2.4.1. Baseline Superimposition.
2.4.2. Ordinary Least Squares Superimposition.
2.4.3. Resistant Fit.
2.5. Representing Shape Differences.
2.5.1. Plotting Superimposed Shape with Wireframe.
2.5.2. Lollipop Diagrams and Vector Fields.
2.5.3. Thin Plate Splines and Warped Shapes.
2.6. Superimposing More Than Two Shapes.
2.6.1. Baseline Registration.
2.6.2. Full Generalized Procrustes Analysis.
2.6.3. Partial Generalized Procrustes Analysis.
2.6.4. Dimensionality of Superimposed Coordinates.
2.7. Exploring Shape Variation and Testing Hypotheses.
2.7.1. PCA.
2.7.2. Multivariate Linear Modelling (Multivariate Regression and MANOVA).
2.7.3. Allometry free approaches (Burnaby correction).
2.7.4. Linear discriminant and Canonical Analysis.
3. Outline Data.
3.1. Acquiring outline Data in R.
3.2. Fourier Analysis.
3.2.1. Principles.
3.2.2. Fourier Analysis of the Tangent Angle.
3.2.3. Radius Fourier Analysis.
3.2.4. Elliptic Fourier Analysis.
3.2.5. Reduction of Shape Variables.
3.2.6. Statistical Analysis of Shape Variation with Fourier Analysis.
3.2.6.1. Exploring Shape Variation and Testing Hypotheses.
3.2.6.2. PCA.
3.2.6.3. Multivariate Linear Modelling (Multivariate Regression and MANOVA).
3.2.6.4. Canonical Analysis.
3.3. Combining Landmarks and Curves.
3.3.1. Hybrid Methods between Fourier and Procrustes Analysis.
3.3.2. Sliding Semi Landmarks.
3.4. Solutions for Open Curves.
4. Specific Applications.
4.1. Testing Measurement Error.
4.2. Partitional Clustering.
4.2.1. K-means, Partition Around Medoids.
4.2.2. Mclust.
4.2.3. Combining Genetic, Geographic and Morphometric Data.
4.3. Modularity / Integration Studies.
4.3.1. Two-block Partial Least Squares.
4.3.2. Testing Among Various Sets of Modules.
4.4.Fluctuating Asymmetry and Directional Asymmetry.
4.4.1. Inter-Individual and Intra-Individual Variation.
4.4.2. Object and Matching Symmetry.
4.5.Bending Energy, Uniform and Non-uniform Shape Variation.
Gonzalez et al. 2016. American Journal of Physical Anthropology
Copyright Oscar Sanisidro