CalcAVG function

Calculates mean correlations within- and between-modules

Uses a binary correlation matrix as a mask to calculate average within- and between-module correlations. Also calculates the ratio between them and the Modularity Hypothesis Index.

CalcAVG(cor.hypothesis, cor.matrix, MHI = TRUE, landmark.dim = NULL)

Arguments

• cor.hypothesis: Hypothetical correlation matrix, with 1s within-modules and 0s between modules
• cor.matrix: Observed empirical correlation matrix.
• MHI: Indicates if Modularity Hypothesis Index should be calculated instead of AVG Ratio.
• landmark.dim: Used if within-landmark correlations are to be excluded in geometric morphometric data. Either 2 for 2d data or 3 for 3d data. Default is NULL for non geometric morphomotric data.

Returns

a named vector with the mean correlations and derived statistics

Examples

# Module vectors
modules = matrix(c(rep(c(1, 0, 0), each = 5),
                   rep(c(0, 1, 0), each = 5),
                   rep(c(0, 0, 1), each = 5)), 15)

# Binary modular matrix
cor.hypot = CreateHypotMatrix(modules)[[4]]

# Modular correlation matrix
hypot.mask = matrix(as.logical(cor.hypot), 15, 15)
mod.cor = matrix(NA, 15, 15)
mod.cor[ hypot.mask] = runif(length(mod.cor[ hypot.mask]), 0.8, 0.9) # within-modules
mod.cor[!hypot.mask] = runif(length(mod.cor[!hypot.mask]), 0.3, 0.4) # between-modules
diag(mod.cor) = 1
mod.cor = (mod.cor + t(mod.cor))/2 # correlation matrices should be symmetric

CalcAVG(cor.hypot, mod.cor)
CalcAVG(cor.hypot, mod.cor, MHI = TRUE)