generate_interactions {aniDom}

Generate simulated interactions with differently-shaped hierarchies

Description

Generates simulated winners and losers. The function can generate data for different population sizes, with differently-shaped hierarchies, and with varying biases. The output is the hierarchy, and the winner and loser for each interaction.

Usage

generate_interactions(N.inds, N.obs, a, b, id.biased = FALSE, rank.biased = FALSE)

Arguments

N.inds

The number of individuals

N.obs

The number of observed interactions (in total).

a

Parameter to control the steepness of the hierarchy (flatter or more sigmoidal) plot_winner_prob.

b

Parameter to control the intercept of the hierachy (moves the sigmoid left or right) plot_winner_prob.

id.biased

Boolean (TRUE/FALSE) describing whether to introduce an individual bias in the observations (some individuals interact more often than others).

rank.biased

Boolean (TRUE/FALSE) describing whether to introduce a rank difference bias in the observations (closely-ranked individuals interact more often).

Details

This function is useful for generating input data with a known hierarchy. The shape of the hierarchy can be controlled using two parameters, though is by default a sigmoidal shape. Higher values of a typically create a greater probability of a dominant winning (turn the function into more of a threshold). Higher values of b typically decrease the probability of a dominant winning when ranks are very similar. The plot_winner_prob function allows visualisation of the hierarchy function (see examples below).

Value

Returns a list with two elements: hierarchy: A dataframe containing three columns, the ID of the individual, its Rank, and its Probability of interacting (varies if id.biased=TRUE). interactions: A dataframe containing two columns, the Winner and the Loser for each interaction. Each row represents one interaction.

Author(s)

Written by Damien R. Farine & Alfredo Sanchez-Tojar

Maintainer: Damien R. Farine <[e-mail]>

References

Sanchez-Tojar, A., Schroeder, J., Farine, D.R. (in prep) Methods for inferring dominance hierarchies and estimating their uncertainty.

Examples

	
	par(mfrow=c(2,2))
	
	# Set population size 
	N <- 20
	
	# Set shape parameters
	a = 15
	b = 3
	
	# See what this looks like
	plot_winner_prob(1:N,a,b)
	
	# Generate some input data
	data <- generate_interactions(N,400,a,b)
	
	# See what the hierarchy looks like from the output data
	winners <- data$interactions$Winner
	losers <- data$interactions$Loser
	identities <- data$hierarchy$ID
	ranks <- data$hierarchy$Rank
	plot_hierarchy_shape(identities,ranks,winners,losers,fitted=TRUE)
	
	# Set new shape parameters
	a = 3
	b = 3
	
	# See what this looks like
	plot_winner_prob(1:N,a,b)
	
	# Generate some input data
	data <- generate_interactions(N,400,a,b)
	
	# See what the hierarchy looks like from the output data
	winners <- data$interactions$Winner
	losers <- data$interactions$Loser
	identities <- data$hierarchy$ID
	ranks <- data$hierarchy$Rank
	plot_hierarchy_shape(identities,ranks,winners,losers,fitted=TRUE)