Generalized Procrustes Analysis
Generalized Procrustes Analysis
GeneralizedProcrustes(x, tolerance = 1e-05, maxiter = 100, Plot = FALSE)
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? |
Generalized Procrustes Analysis for several configurations contained in a three-dimensional array.
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 |
Jose Luis Vicente-Villardon
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
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)
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