elo.model1: Optimize k parameter in Elo rating method

In EloOptimized: Optimized Elo Rating Method for Obtaining Dominance Ranks

Description

Function to optimize k parameter in Elo Rating Method

Usage

elo.model1(par, burn_in=100, init_elo = 1000, IA_data, all_ids, p_function = "sigmoid", 
  return_likelihood = T)

Arguments

par	initial value of log(k)
burn_in	burn in period for establishing initial elo scores. Defaults to 100
init_elo	Initial Elo score for all individuals. Defaults to 1000
IA_data	Data frame with Date, Winner, and Loser
all_ids	list of all IDs in sample
p_function	function used to calculate probability of winning. Defaults to sinusoidal function, but use "pnorm" to use the pnorm-based method implemented in the EloRating package.
return_likelihood	Logical; if TRUE, returns log likelihood based on given par, if FALSE returns agonistic interactions table with elo scores based on given value of par

Examples

#for internal use

EloOptimized documentation built on June 10, 2021, 5:06 p.m.