Function for Principal Coordinate Analysis
Function PrinCoor implements Principal Coordinate Analysis, also known as classical metric multidimensional scaling or
classical scaling. In comparison with other software, it offers refined statistics for goodness-of-fit at the level of individual observations and pairs of observartions.
PrinCoor(Dis, eps = 1e-10)
Dis |
A distance matrix or dissimilarity matrix |
eps |
A tolerance criterion for deciding if eigenvalues are zero or not |
Calculations are based on the spectral decomposition of the scalar product matrix B, derived from the distance matrix.
X |
The coordinates of the the solution |
la |
The eigenvalues of the solution |
B |
The scalar product matrix |
standard.decom |
Standard overall goodness-of-fit table using all eigenvalues |
positive.decom |
Overall goodness-of-fit table using only positive eigenvalues |
absolute.decom |
Overall goodness-of-fit table using absolute values of eigenvalues |
squared.decom |
Overall goodness-of-fit table using squared eigenvalues |
RowStats |
Detailed goodness-of-fit statistics for each row |
PairStats |
Detailed goodness-of-fit statistics for each pair |
Jan Graffelman jan.graffelman@upc.edu
Graffelman, J. (2019) Goodness-of-fit filtering in classical metric multidimensional scaling with large datasets. <doi: 10.1101/708339>
Graffelman, J. and van Eeuwijk, F.A. (2005) Calibration of multivariate scatter plots for exploratory analysis of relations within and between sets of variables in genomic research Biometrical Journal, 47(6) pp. 863-879.
data(spaindist) results <- PrinCoor(as.matrix(spaindist))
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