geomorph (version 2.0.1)

compare.modular.partitions: Compare modular signal to alternative landmark subsets

Description

Function quantifies the degree of morphological integration between two or more modules of Procrustes-aligned landmark coordinates and compares this to patterns found by randomly assigning landmarks into subsets

Usage

compare.modular.partitions(A, landgroups, iter = 999)

Arguments

A

An array (p x k x n) containing GPA-aligned coordinates for all specimens

landgroups

A list of which landmarks belong in which partition (e.g. A,A,A,B,B,B,C,C,C)

iter

Number of iterations for significance testing

Value

• Function returns a list with the following components:
• RV
  The estimate of morphological integration
• pvalue
  The significance level of the observed signal
• RV.min
  The minimal RV coefficient found via landmark permutation
• RV.min.partitions
  A list of landmarks assigned to partitions that yields the minimal RV coefficient

Details

The function quantifies the degree of morphological integration between two or more modules of shape data as defined by landmark coordinates, and compares this to modular signals found by randomly assigning landmarks to modules. It is assumed that the landmarks have previously been aligned using Generalized Procrustes Analysis (GPA) [e.g., with gpagen]. The degree of morphological integration is quantified using the RV coefficient (Klingenberg 2009). If more than two modules are defined, the average RV coefficient is utilized (see Klingenberg 2009). The RV coefficient for the observed modular hypothesis is then compared to a distribution of values obtained by randomly assigning landmarks into subsets, with the restriction that the number of landmarks in each subset is identical to that observed in each of the original partitions. A significant modular signal is found when the observed RV coefficient is small relative to this distribution (see Klingenberg 2009). A histogram of coefficients obtained via resampling is presented, with the observed value designated by an arrow in the plot. Landmark groups can be defined using define.modules.

References

Klingenberg, C. P. 2009. Morphometric integration and modularity in configurations of landmarks: tools for evaluating a priori hypotheses. Evol. Develop. 11:405-421.

Examples

data(plethodon)
Y.gpa<-gpagen(plethodon$land)    #GPA-alignment
 #landmarks on the skull and mandible assigned to partitions
land.gps<-c("A","A","A","A","A","B","B","B","B","B","B","B")

compare.modular.partitions(Y.gpa$coords,land.gps,iter=99)
#Result implies that the skull and mandible are not independent modules