calculate ratings from sequence of rating events, allowing for more than two stimuli
triplets(xdata, winner, runs = 2, startvalue = 0, k = 100,
progressbar = TRUE, mode = "avg")data.frame or matrix with stimulus IDs, each row representing one trial, needs to contain at least two columns
numeric vector of the same length as nrow(xdata), indicating which column in xdata is the winner/preferred stimulus
numeric, the number of times the data set should be randomized
numeric, initial value of ratings, by default 0
numeric, value of k-constant
logical, by default TRUE. Should a progress bar be displayed
character, either "avg" (default) or "seq", see Details section
a matrix with ratings
The mode="avg" option considers the losers of the trial as one individual/stimulus, whose rating is averaged. This reflects one rating step for each trial (as for elochoice()).
The mode="seq" option runs a sequence of interactions within a trial, i.e. one rating step for each of the loosing stimuli. E.g. if you have three stimuli, that would be two rating steps. With four stimuli, we would have three steps, etc.
Because of the larger number of rating events with mode="seq", the range of Elo-ratings will be larger as compared to mode="avg". The average values will be the same for both though (start value). See examples...
Also note that this is an experimental function that has not yet been tested thoroughly! In addition, this function calculates winning probabilities in a slightly different way as compared to elochoice, i.e. based on normal probabilities (see elochoice).
# NOT RUN {
data(physical)
y <- round(triplets(physical[, 2:3], winner = rep(1,nrow(physical)), runs = 1))
x <- ratings(elochoice(physical$Winner, physical$Loser, runs = 1), show = "all", drawplot = FALSE)
x <- x[order(names(x))]
plot(x, y)
xdata <- as.matrix(t(sapply(1:500, function(x)sample(letters[1:8], 3))))
xdata <- t(apply(xdata, 1, sort))
winner <- sample(1:3, nrow(xdata), TRUE, prob = c(4, 0.8, 0.1))
x <- triplets(xdata, winner, runs=20, mode="avg")
y <- triplets(xdata, winner, runs=20, mode="seq")
# note different ranges along the axes
plot(colMeans(x), colMeans(y))
range(colMeans(x))
range(colMeans(y))
# }