joint_gregariousness: Joint gregariousness

In MNWeiss/aninet: Statistical Models for Animal Social Networks

Joint gregariousness

Description

This function calculates the "joint gregariousness" for each dyad, as given by Whitehead & James (2015)

Usage

joint_gregariousness(network, log.jg = T)

Arguments

network	A square matrix, defining the network from which to calculate joint gregariousness
log.jg	Logical, indicating whether the log-transformed scores are to be returned.

Details

Joint gregariousness is defined by Whitehead & James (2015) as:

joint gregariousness[i,j] = log(greg[i] * greg[j])

, where greg[i] is

∑_{k \neq j} network[i,k]

This definition removes each dyads edge weight from the calculation of their joint gregariousness. The function allows the user to get the non log-transformed value. This is useful if there are individuals with only as single non-zero edge weight.

Value

A square matrix of dyadic joint gregariousness scores.

MNWeiss/aninet documentation built on Jan. 31, 2023, 3:55 a.m.