proCrustes: Comparison of n-point Configurations vis Procrustes Analysis

In mvdalab: Multivariate Data Analysis Laboratory

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, ...)

Arguments

X	Target configuration
Y	Matching configuration
scaling	Scale Y-axis
standardize	Standardize configurations
scale.unit	Scale to unit variance
...	additional arguments. Currently ignored.

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

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

Author(s)

Nelson Lee Afanador ([e-mail])

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)

mvdalab documentation built on Oct. 6, 2022, 1:05 a.m.