mvdalab (version 1.7)

proCrustes: Comparison of n-point Configurations vis Procrustes Analysis

Description

Implementation of Procrustes Analysis in the spirit of multidimensional scaling.

Usage

proCrustes(X, Y, scaling = TRUE, standardize = FALSE, scale.unit = F, ...)

Value

Rotation.Matrix

The matrix, Q, that rotates Y towards X; obtained via svd of X'Y

Residuals

residuals after fitting

M2_min

Residual Sums of Squares

Xmeans

Column Means of X

Ymeans

Column Means of Y

PRMSE

Procrustes Root Mean Square Error

Yproj

Projected Y-values

scale

logical. Should Y be scaled.

Translation

Scaling through a common distance based on rotation of Y and scaling parameter, c

residuals.

residual sum-of-squares

Anova.MSS

Explained Variance w.r.t. Y

Anova.ESS

Unexplained Variance w.r.t. Y

Anova.TSS

Total Sums of Squares w.r.t. X

Arguments

X

Target configuration

Y

Matching configuration

scaling

Scale Y-axis

standardize

Standardize configurations

scale.unit

Scale to unit variance

...

additional arguments. Currently ignored.

Author

Nelson Lee Afanador ()

Details

This function implements Procrustes Analysis as described in the reference below. That is to say:

Translation: Fixed displacement of points through a constant distance in a common direction

Rotation: Fixed displacement of all points through a constant angle

Dilation: Stretching or shrinking by a contant amount

References

Krzanowski, Wojtek. Principles of multivariate analysis. OUP Oxford, 2000.

Examples

X <- iris[, 1:2]
Y <- iris[, 3:4]

proc <- proCrustes(X, Y)
proc
names(proc)