eloratingfixed: Create daily elo ranks and multiple derivatives with...
In EloOptimized: Optimized Elo Rating Method for Obtaining Dominance Ranks

Description Usage Arguments Details Value Examples

Conducts traditional elo rating analyses using specified K value and outputs raw, normalized, cardinal, and categorical ranks as a list object in R or in an output file. For optimized Elo parameters, use eloratingopt.

1 2	eloratingfixed(agon_data, pres_data, k = 100, init_elo = 1000, outputfile = NULL, returnR = TRUE, p_function = "sigmoid")

`agon_data`	Input data frame with dominance interactions, should only contain Date, Winner, Loser. Date should be formatted as MONTH/DAY/YEAR, or already as Date class.
`pres_data`	Input data frame with columns "id", "start_date" and "end_date". Date columns should be formatted as MONTH/DAY/YEAR, or already as Date class. If all IDs are present the whole time, you ignore this and a pres_data table will be automatically generated.
`k`	Specified value of the k parameter, default is 100
`init_elo`	The starting Elo value for all individuals, default is 1000
`outputfile`	Name of csv file to save ranks to. Default is NULL, in which case the function will only return a table in R. If you supply an output file name the function will save the results as a csv file in your working directory.
`returnR`	whether to return an R object from the function call. Default is TRUE
`p_function`	function defining probability of winning. Default "sigmoid" is equation (1) from Foerster, Franz et al 2016. Use "pnorm" to use the `pnorm`-based method implemented in the EloRating package.

This function accepts a data frame of date-stamped dominance interactions and (optionally) a data frame of start and end dates for each individual to be ranked, and outputs daily Elo scores with parameters specified by the user. The default function used to determine probability of winning is equation (1) from Foerster, Franz et al. 2016, but for ease of comparison with the EloRating package, we also added the option to use the pnorm-based method implemented in the EloRating package, and future development will add the option to use the original function from Elo 1978 (as implemented in the elo package). This function does not require large presence matrices, and efficiently calculates a series of additional indices (described below).

As opposed to the eloratingopt function, this procedure only requires that included individuals have at least one win or one loss.

A detailed description of the function output is given in the Value section of this help file:

Returns a list with six elements:

elo Data frame with all IDs and dates they were present, with the following columns:
- Date: Dates of study period
- Individual: the names of each ranked individual, for each date they were present
- Elo: fitted Elo scores for each individual on each day
- EloOrdinal: Daily ordinal rank based on Elo scores
- EloScaled: Daily Elo scores rescaled between 0 and 1 according to
  
  ([individual Elo] - min([daily Elo scores])/(max([daily Elo scores]) - min([daily Elo scores]))
- ExpNumBeaten: expected number of individuals in the group beaten, which is the sum of winning probabilities based on relative Elo scores of an individual and all others, following equation (4) in Foerster, Franz et al. 2016
- EloCardinal: ExpNumBeaten values rescaled as a percentage of the total number of ranked individuals present in the group on the day of ranking. We encourage the use of this measure.
- JenksEloCardinal: Categorical rank (high, mid, or low) using the Jenks natural breaks classification method implemented in the R package BAMMtools. See getJenksBreaks
k User-defined value of the k parameter
init_elo User-defined initial Elo score when individuals enter the hierarchy
pred_accuracy Proportion of correctly predicted interactions
logL The overall log-likelihood of the observed data given the user-supplied parameter values based on winning probabilities (as calculated in equation (1) of Foerster, Franz et al 2016) for all interactions

nbadata = EloOptimized::nba #nba wins and losses from the 1995-96 season
nbaelo = eloratingfixed(agon_data = nbadata)
# generates traditional Elo scores (with init_elo = 1000 & k = 100) and saves 
#   them as "nbaelo"