19. Desiderata
An ideal distance metric should:
1. have high fidelity
2. be normally distributed
20. Desiderata
An ideal distance metric should:
1. have high fidelity
2. be normally distributed
3. be Euclidean
21. Desiderata
An ideal distance metric should:
1. have high fidelity
2. be normally distributed
3. be Euclidean
4. be calculable
22. Desiderata
An ideal distance metric should:
1. have high fidelity
2. be normally distributed
3. be Euclidean
4. be calculable
5. be easily visualised
31. Simulations
Input Output
20 taxa
50 binary characters
0-80% missing data
N taxa retained
Variance of first two PCA axes
Shapiro-Wilk test
Mantel test
32. Calculable
Raw GED
Gower
MOD
Incompleteness
100%
% taxa retained
0% 80%
0%
58. Toljagic
and
Butler
2013
Disparity time series
Brusatte et al
2008
Thorne
et al
2011
Butler et al. 2011
59. Toljagic
and
Butler
2013
Disparity time series
4 time bins 4 time bins
Brusatte et al
2008
Thorne
et al
2011
14 time bins 2 time bins
Butler et al. 2011
60. Rate time series
Lloyd et al 2012
Ruta et al
2006
Branch-binning No completeness
Fidelity (distances in ordination = true distances)
Normality (no outliers)
Distances should be Euclidean (low/no negative eigenvalues)
High variance on first two ordination axes (visualisation)
Retain as many taxa as possible (calculable distances)
Fidelity (distances in ordination = true distances)
Normality (no outliers)
Distances should be Euclidean (low/no negative eigenvalues)
High variance on first two ordination axes (visualisation)
Retain as many taxa as possible (calculable distances)
Fidelity (distances in ordination = true distances)
Normality (no outliers)
Distances should be Euclidean (low/no negative eigenvalues)
High variance on first two ordination axes (visualisation)
Retain as many taxa as possible (calculable distances)
Fidelity (distances in ordination = true distances)
Normality (no outliers)
Distances should be Euclidean (low/no negative eigenvalues)
High variance on first two ordination axes (visualisation)
Retain as many taxa as possible (calculable distances)
Fidelity (distances in ordination = true distances)
Normality (no outliers)
Distances should be Euclidean (low/no negative eigenvalues)
High variance on first two ordination axes (visualisation)
Retain as many taxa as possible (calculable distances)
Fidelity (distances in ordination = true distances)
Normality (no outliers)
Distances should be Euclidean (low/no negative eigenvalues)
High variance on first two ordination axes (visualisation)
Retain as many taxa as possible (calculable distances)