elo_steepness_from_sequence: steepness based on Bayesian Elo-rating

In EloSteepness: Bayesian Dominance Hierarchy Steepness via Elo Rating and David's Scores

steepness based on Bayesian Elo-rating

Description

for interaction data with known sequence of observations

Usage

elo_steepness_from_sequence(
  winner,
  loser,
  algo = c("fixed_sd", "original", "fixed_k"),
  silent = FALSE,
  k = NULL,
  ...
)

Arguments

winner	character (or factor) of winning individuals
loser	character (or factor) of losing individuals
algo	character, either "fixed_sd", "original", or "fixed_k".This determines which algorithm to estimate Elo-ratings is used. Default is "fixed_sd", which is a slight modification from Goffe et al's original code. "fixed_k" fixes the k parameter ('shift coefficient' in Goffe et al) to the set value rather than estimating it from the data.
silent	logical, suppress warnings (default is FALSE)
k	numeric, provides a fixed k parameter. This only has effects if algo = "fixed_k". At its default NULL a value of 0.4 is used.
...	additional arguments for sampling()

Value

a list with results of the model fitting (see elo_steepness_from_matrix) for details

Examples

data(adv, package = "EloRating")
res <- elo_steepness_from_sequence(winner = adv$winner, loser = adv$loser,
                                   cores = 1, chains = 2, iter = 1000, 
                                   warmup = 500, seed = 1, refresh = 0)
plot_steepness(res)

EloSteepness documentation built on Sept. 21, 2023, 1:06 a.m.