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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
```{r,echo = FALSE}
library(EloOptimized)
```
# EloOptimized
<!-- badges: start -->
[](https://github.com/jtfeld/EloOptimized/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=EloOptimized)
[](https://cran.r-project.org/package=EloOptimized)
<!-- badges: end -->
[Package website](https://jtfeld.github.io/EloOptimized/)
EloOptimized provides tools to implement the maximum likelihood methods for deriving Elo scores as published in [Foerster, Franz et al. (2016). Chimpanzee females queue but males compete for social status](https://www.nature.com/articles/srep35404). In addition, it provides functionality to efficiently generate traditional Elo scores using a simplified procedure that doesn't require the use of cumbersome presence matrices. Finally, it quickly generates a number of additional Elo-based indices (ordinal, normalized, cardinal, and categorical ranks and rank scores) of potential use to researchers, as outlined in the linked manuscript.
## Installation
```{r gh-installation, eval = FALSE}
# Current version on Github:
# install.packages("devtools")
devtools::install_github("jtfeld/EloOptimized")
# CRAN-approved version on CRAN:
install.packages("EloOptimized")
```
## Example
There are two functions of interest. Use eloratingopt() to calculate Elo scores using optimized Elo parameter values, or eloratingfixed() to calculate Elo scores using user-defined parameter values.
```{r example, eval = FALSE}
# to generate Elo scores using fixed initial Elo scores (1000) and a ML-fitted value for the K parameter:
nbaelo = eloratingopt(agon_data = nba, fit_init_elo = FALSE)
# to generate Elo scores using fixed default initial Elo scores and default K:
nbaelo = eloratingfixed(agon_data = nba, k = 100, init_elo = 1000)
```
To recreate the results from the 2016 manuscript, use the following code:
```{r MS example, eval = FALSE}
# Males, model type 1:
melo1 = eloratingopt(agon_data = chimpagg_m, pres_data = chimppres_m, fit_init_elo = F)
# Males, model type 3:
melo3 = eloratingopt(agon_data = chimpagg_m[101:nrow(chimpagg_m),],
pres_data = chimppres_m, fit_init_elo = T)
# Females, model type 1:
felo1 = eloratingopt(agon_data = chimpagg_f, pres_data = chimppres_f, fit_init_elo = F)
# Females, model type 3:
felo3 = eloratingopt(agon_data = chimpagg_f[101:nrow(chimpagg_f),],
pres_data = chimppres_f, fit_init_elo = T)
```