Comments (4)
Questions
- what to do for 2D samples? Have only XY columns, or add Z=0?
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3D plot:
- use FM (pairwise distances) with MDS,
- make a 3D plot with {rgl}
- color edges between landmarks according to >1 or <1 and CI based significance
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Parametric estimation of SigmaK:
- SigmaK estimation + identifiability checks
- use bootstrap results from object to estimate uncertainty for SigmaK
- add a method to evaluate sensitivity to initial values
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Here is a more detailed roadmap for outstanding features and modifications:
Nonparametric estimation
- remove mixed bootstrap, allow choosing reference (num, denom) for global T test
- find alternate naming (not T or F)
Visualization
- find suitable divergent palette, option to change palette
- implement 3D plot
Data checking
- point cloud around landmarks (as in MorphoJ)
- mean form + 1 individual at a time (indiv ID prnted), or use average?
Parametric estimation
- check landmark names when working with pattern matrix
- make non-estimability error optimal (i.e. allow exploration)
- allow parameter names to be numeric (i.e. numbers as characters)
classification/clustering
- use FM based log(T)-distances for HCA/MDS/k-means to see if there are groups
- predetermined groups, where does it belong? Wishart based likelihood distance ranking
Influential landmark
- LOO type assessment, potentially leaving out multiple landmarks and assess global fit / CI?
GM/GDM revisions
- GM/GDM revisions
Docs
- add Rd pages
- updated tutorial (JSS style)
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