geomorph (version 2.1.6)

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)

Usage

two.b.pls(A1, A2, warpgrids = TRUE, iter = 999, verbose = FALSE,
  label = NULL, ShowPlot = TRUE)

Arguments

A1

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

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)

iter

Number of iterations for significance testing

verbose

A logical value indicating whether the output is basic or verbose (see Value below)

label

An optional vector indicating labels for each specimen that are to be displayed

ShowPlot

A logical value indicating whether or not a plot of Procrustes residuals should be displayed

Value

• Function returns a list with the following components:
• value
  The estimate of association between block
• pvalue
  The significance level of the observed association
• Xscores
  PLS scores for the first block of landmarks (when {verbose=TRUE})
• Yscores
  PLS scores for the second block of landmarks (when {verbose=TRUE})
• left.singular.coef
  Left matrix singular value coefficients (loadings) for principal vector (when {verbose=TRUE})
• right.singular.coef
  Right matrix singular value coefficients (loadings) for principal vector (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). If geometric morphometric data are used, it is assumed that the landmarks have previously been aligned using Generalized Procrustes Analysis (GPA) [e.g., with gpagen]. If other variables are used, they must be input as a 2-Dimensional matrix (rows = specimens, columns = variables). 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.

Examples

data(plethShapeFood)
Y.gpa<-gpagen(plethShapeFood$land)    #GPA-alignment

#2B-PLS between head shape and food use data
two.b.pls(Y.gpa$coords,plethShapeFood$food,iter=99)