PrinCoor: Function for Principal Coordinate Analysis

In calibrate: Calibration of Scatterplot and Biplot Axes

Description

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.

Usage

PrinCoor(Dis, eps = 1e-10)

Arguments

Dis	A distance matrix or dissimilarity matrix
eps	A tolerance criterion for deciding if eigenvalues are zero or not

Details

Calculations are based on the spectral decomposition of the scalar product matrix B, derived from the distance matrix.

Value

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

Author(s)

Jan Graffelman [e-mail]

References

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.

See Also

princomp

Examples

   data(spaindist)
   results <- PrinCoor(as.matrix(spaindist))

calibrate documentation built on July 1, 2020, 7:03 p.m.