optistart: optimize start values

In gobbios/EloRating: Animal Dominance Hierarchies by Elo Rating

optimize start values

Description

experimental function to test different sets of randomly selected start values

Usage

optistart(
  eloobject,
  burnin = 0,
  spread = 200,
  runs = 2000,
  doplot = FALSE,
  initialcohort = TRUE
)

Arguments

eloobject	output from elo.seq
burnin	numeric, the number of interactions to be excluded from the calculation of the (log) likelihood. This parameter is ignored if a date range is supplied. By default burnin = 0, i.e. all interactions are considered.
spread	numeric, the standard deviation of the ratings to be tested (by default 200)
runs	numeric, number of inital ratings to be tested (by default 2000)
doplot	logical, should the distribution of log likelihoods be plotted
initialcohort	logical, not yet implemented

Details

if the plot is produced, the red line indicates the log-likelihood when all individuals are assigned the same starting value

the item $best reflects the optimal start values found

Value

a list with multiple items:

Author(s)

Christof Neumann

Examples

set.seed(123)
xdata <- randomsequence(8, 100)$seqdat
res1 <- elo.seq(xdata$winner, xdata$loser, xdata$Date)
ores <- optistart(res1)
res2 <- elo.seq(xdata$winner, xdata$loser, xdata$Date, startvalue = ores$best)
eloplot(res1)
eloplot(res2)

gobbios/EloRating documentation built on June 4, 2023, 6:33 a.m.