two.b.pls: Two-block partial least squares analysis for shape data
Description
Function performs two-block partial least squares analysis
to assess the degree of association between to blocks of
Procrustes-aligned coordinates (or other variables)
A matrix (n x [p x k]) or 3D array (p x k x n)
containing GPA-aligned coordinates for the first block
A2
A matrix (n x [p x k]) or 3D array (p x k x n)
containing GPA-aligned coordinates for the second block
iter
Number of iterations for significance
testing
warpgrids
A logical value indicating whether
deformation grids for shapes along PC1 should be
displayed (only relevant if data for A1 or A2 [or both]
were input as 3D array)
verbose
A logical value indicating whether the
output is basic or verbose (see Value below)
Value
Function returns a list with the following components:
valueThe estimate of association between block
pvalueThe significance level of the observed
association
XscoresPLS scores for the first block
of landmarks (when {verbose=TRUE})
YscoresPLS
scores for the second block of landmarks (when
{verbose=TRUE})
Details
The function quantifies the degree of association between
two blocks of shape data as defined by landmark coordinates
using partial least squares (see Rohlf and Corti 2000). It
is assumed that the landmarks have previously been aligned
using Generalized Procrustes Analysis (GPA) [e.g., with
gpagen].
A plot of PLS scores from Block1 versus Block2 is provided
for the first set of PLS axes. Thin-plate spline
deformation grids along these axes are also shown (if data
were input as a 3D array).
References
Rohlf, F.J., and M. Corti. 2000. The use of partial
least-squares to study covariation in shape. Systematic
Biology 49: 740-753.
data(plethShapeFood)
Y.gpa<-gpagen(plethShapeFood$land) #GPA-alignment#2B-PLS between head shape and food use datatwo.b.pls(Y.gpa$coords,plethShapeFood$food,iter=99)