proCrustes {mvdalab}

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)