Description Usage Arguments Details Value Examples

`bradleyTerry`

Computes the MLE for the BT model using an MM algorithm

1 | ```
bradleyTerry(conf.mat, initial = NA, baseline = NA, stop.dif = 0.001)
``` |

`conf.mat` |
a matrix of conf.mat class. An N-by-N conflict matrix whose |

`initial` |
initial values of dominance indices for the MM algorithm, if not supplied, the 0 vector will be the inital value. |

`baseline` |
index for agent to represent baseline dominance index set to 0. If NA, the "sum-to-one" parameterization will be used. |

`stop.dif` |
numeric value for difference in log likelihood value between iterations. Used as the convergence criterion for the algorithm. |

In order to meet Bradley-Terry assumption, each ID in `conf.mat`

should have at least one win AND one loss.
`bradleyTerry`

will return an error if no more than one win or loss was found.

@references Shev, A., Hsieh, F., Beisner, B., & McCowan, B. (2012). Using Markov chain Monte Carlo (MCMC) to visualize and test the linearity assumption of the Bradley-Terry class of models. Animal behaviour, 84(6), 1523-1531.

Shev, A., Fujii, K., Hsieh, F., & McCowan, B. (2014). Systemic Testing on Bradley-Terry Model against Nonlinear Ranking Hierarchy. PloS one, 9(12), e115367.

A list of length 3.

`domInds` |
a vector of length N consiting of the MLE values of the dominance indices. Lower values represent lower ranks. |

`probMat` |
an N-by-N numeric matrix of win-loss probabilities estimated by the BT model. |

`logLik` |
the model fit. |

1 2 3 4 5 6 7 8 9 | ```
# create an edgelist
edgelist1 <- data.frame(col1 = sample(letters[1:15], 200, replace = TRUE),
col2 = sample(letters[1:15], 200, replace = TRUE),
stringsAsFactors = FALSE)
edgelist1 <- edgelist1[-which(edgelist1$col1 == edgelist1$col2), ]
# convert an edgelist to conflict matrix
confmatrix_bt <- as.conflictmat(edgelist1)
# Computes the MLE for the BT model
bt <- bradleyTerry(confmatrix_bt)
``` |

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