Monday, January 23rd, 2017.
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.
Tuesday, January 24th, 2017.
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.
2.3.2. Scaling using Baseline or Centroid Size.
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.