calibrate (version 1.7.7)

PrinCoor: Function for Principal Coordinate Analysis

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

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

Details

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

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

# NOT RUN {
   data(spaindist)
   results <- PrinCoor(as.matrix(spaindist))
# }