geomorph (version 2.0.1)

phylo.pls: Quantify phylogenetic morphological integration between two sets of variables

Description

Function quantifies the degree of phylogenetic morphological covariation between two sets of Procrustes-aligned coordinates using partial least squares.

Usage

phylo.pls(A1, A2, phy, warpgrids = TRUE, iter = 999, verbose = FALSE)

Arguments

A1

A 2D array (n x [p1 x k1]) or 3D array (p1 x k1 x n) containing landmark coordinates for the first block

A2

A 2D array (n x [p2 x k2]) or 3D array (p2 x k2 x n) containing landmark coordinates for the second block

phy

A phylogenetic tree of {class phylo} - see read.tree in library ape

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)

Value

• Function returns a list with the following components:
• PLS Correlation
  The estimate of phylogenetic morphological covariation
• pvalue
  The significance level of the observed signal
• Block 1 PLS Scores
  PLS scores for the first block of landmarks (when {verbose=TRUE})
• Block 2 PLS Scores
  PLS scores for the second block of landmarks (when {verbose=TRUE})

Details

The function quantifies the degree of phylogenetic morphological integration between two sets of shape data as defined by landmark coordinates. It is assumed that the landmarks have previously been aligned using Generalized Procrustes Analysis (GPA) [e.g., with gpagen]. The function estimates the degree of morphological covariation between two sets of variables while accounting for phylogeny using partial least squares (Adams and Felice 2014). The observed value is statistically assessed using permutation, where data for one block are permuted across the tips of the phylogeny, an estimate of the covariation between sets of variables, and compared to the observed value. 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

Adams, D.C. and R. Felice. 2014. Assessing phylogenetic morphological integration and trait covariation in morphometric data using evolutionary covariance matrices. PLOS ONE. 9(4):e94335.

Examples

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

phylo.pls(Y.gpa$coords[1:5,,],Y.gpa$coords[6:11,,],plethspecies$phy,iter=5)