HWI: Half-Weight Index

In assocInd: Implements New and Existing Association Indices for Constructing Animal Social Networks

Description

Calculates the half-weight index

Usage

HWI(x, Ya, Yb, Yab)

Arguments

x	Number of times individuals a and b were observed together
Ya	Number of times individual a was observed without b
Yb	Number of times individual b was observed without a
Yab	Number of times individuals a and b were observed at the same time but not associating

Details

The half-weight index calculates the probability that two individuals are observed together given that one has been seen, but under the assumption that the probability of missing individuals a or b when they are together is equal to M times the probability of missing them when they are apart. This index is the most commonly used in animal social network analysis, but note that Hoppitt & Farine (in prep) and other authors argue that the weighting of the probabilities is arbitrary.

Value

Returns two elements: the estimated association strength and the standard error of the estimate.

Author(s)

William Hoppitt <[e-mail]> Damien Farine <[e-mail]>

References

Hoppitt, W. & Farine, D.R. (in prep) Association indices for quantifying social relationships: how to deal with missing observations of individuals or groups.

Examples

	
	# Simulated values (all = 10)
	x <- ya <- yb <- yab <- 10
	
	# Calculate half-weight index
	HWI(x,ya,yb,yab)
	

Example output

[1] 0.3333333 0.0860663

assocInd documentation built on May 2, 2019, 11:42 a.m.