simple_steep_gen {EloSteepness}

generate dominance interactions with specified steepness

Description

generate dominance interactions with specified steepness

Usage

simple_steep_gen(
  n_ind,
  n_int,
  steep,
  id_bias = 0,
  rank_bias = 0,
  sequential = TRUE
)

Arguments

n_ind

integer, the number of individuals

n_int

integer, the number of interactions

steep

numeric (between 0 and 1), the desired steepness value

id_bias

numeric, between 0 and 1. If 0 all individual are equally likely to interact. If 1, some individuals have higher propensities to interact.

rank_bias

numeric, between 0 and 1. If 0 there is no relationship between rank distance and interaction propensity. If 1 there is a strong relationship: dyads closer in rank interact more often.

sequential

logical, default is TRUE. See details.

Details

Initially (and this is still the default), the function generated interactions and their outcomes sequentially: first a dyad was chosen that interacted and then its winner was determined. This was repeated for as many interactions as set by n_int=.

The same results can be achieved much more efficiently by first setting the number of interactions per dyad and then looping through all dyads and then generate the interactions and their outcomes per dyad. This can be achieved by setting sequential = FALSE. In this latter case the 'sequence' of interactions reported in the results is just a randomized version of all interactions, whereas in the former case there is a 'natural sequence' (although it is meaningless because the sequence is irrelevant with respect to outcomes of individual interactions (the system is stable)).

Value

a list with the first item being the interactions in sequence form ($sequence). The second item ($matrix) is the square interaction matrix and the third item ($settings) is a list with input settings (including probabilities to interact for each dyad).

Examples

res <- simple_steep_gen(n_ind = 5, n_int = 30, steep = 0.99)
res$sequence
res$matrix


library(EloRating)
steeps <- runif(20, 0, 1)
nids <- sample(6:10, length(steeps), TRUE)
mats <- sapply(1:length(steeps), function(x) {
  simple_steep_gen(nids[x], nids[x] ^ 2.5, steeps[x], 0)[[2]]
 })
obs_steeps <- unlist(lapply(mats, function(x)steepness(x)[1]))
plot(steeps, obs_steeps, xlim = c(0, 1), ylim = c(0, 1))
abline(0, 1)