GeneralizedProcrustes: Generalized Procrustes Analysis

In villardon/MultBiplotR: Multivariate Analysis Using Biplots in R

Description

Generalized Procrustes Analysis

Usage

  GeneralizedProcrustes(x, tolerance = 1e-05, maxiter = 100, Plot = FALSE)

Arguments

x	Three dimensional array with the configurations. The first dimension contains the rows of the configurations, the second contains the columns and the third the number of configurations. So x[,,i] is the i-th configuration
tolerance	Tolerance for the Procrustes algorithm.
maxiter	Maximum number of iterations
Plot	Should the results be plotted?

Details

Generalized Procrustes Analysis for several configurations contained in a three-dimensional array.

Value

An object of class GenProcustes.This has components:

History	History of Iterations
X	Initial configurations in a three dimensional array
RotatedX	Transformed configurations in a three dimensional array
Scale	Scale factors for each configuration
Rotations	Rotation Matrices in a three dimensional array
rss	Residual Sum of Squares
Fit	Goodness of fit as percent of expained variance

Author(s)

Jose Luis Vicente-Villardon

References

Gower, J.C., (1975). Generalised Procrustes analysis. Psychometrika 40, 33-51.

Ingwer Borg, I. & Groenen, P. J.F. (2005). Modern Multidimensional Scaling. Theory and Applications. Second Edition. Springer

See Also

PrincipalCoordinates

Examples

data(spiders)
n=dim(spiders)[1]
p=dim(spiders)[2]
prox=array(0,c(n,2,4))

p1=BinaryProximities(spiders,coefficient=5)
prox[,,1]=PrincipalCoordinates(p1)$RowCoordinates
p2=BinaryProximities(spiders,coefficient=2)
prox[,,2]=PrincipalCoordinates(p2)$RowCoordinates
p3=BinaryProximities(spiders,coefficient=3)
prox[,,3]=PrincipalCoordinates(p3)$RowCoordinates
p4=BinaryProximities(spiders,coefficient=4)
prox[,,4]=PrincipalCoordinates(p4)$RowCoordinates
GeneralizedProcrustes(prox)

villardon/MultBiplotR documentation built on June 5, 2021, 8:55 a.m.