Description Usage Arguments Value Author(s) References Examples
calculate Elo ratings for a single interaction
1  e.single(ELO1old, ELO2old, outcome, k = 100)

ELO1old 
Elo rating of the first individual 
ELO2old 
Elo rating of the second individual 
outcome 
"1" = first individual wins and second looses 
k 
k factor 
length of vector 2 with updated ratings after the interaction of first and second individual
Christof Neumann
Albers, P. C. H. & de Vries, H. 2001. Elorating as a tool in the sequential estimation of dominance strengths. Animal Behaviour, 61, 489495.
Neumann, C., Duboscq, J., Dubuc, C., Ginting, A., Irwan, A. M., Agil, M., Widdig, A. & Engelhardt, A. 2011. Assessing dominance hierarchies: validation and advantages of progressive evaluation with Elorating. Animal Behaviour, 82, 911921.
1 2 3 4 5 6 7 8 9 10 11  e.single(ELO1old=1200, ELO2old=1000, outcome=1, k=100)
# same as before
e.single(ELO1old=1000, ELO2old=1200, outcome=2, k=100)
# an undecided interaction
e.single(ELO1old=1200, ELO2old=1000, outcome=0, k=100)
# if rating differences are too big, no change occurs
# if higherrated individual wins
e.single(ELO1old=2000, ELO2old=1000, outcome=1, k=100)
# same as before but lowerrated individual wins and
# therefore wins maximum number of points possible (i.e. k)
e.single(ELO1old=2000, ELO2old=1000, outcome=2, k=100)

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