PrinCoor {calibrate} | R Documentation |
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))