# citation statistics: being cited is a 'win'; citing is a 'loss'
journal <- c("Biometrika", "Comm.Statist", "JASA", "JRSS-B")
mat <- matrix(c( NA, 33, 320, 284,
730, NA, 813, 276,
498, 68, NA, 325,
221, 17, 142, NA), 4, 4)
dimnames(mat) <- list(winner = journal, loser = journal)
# Add some ties. This is fictitional data.
ties <- 5 + 0 * mat
ties[2, 1] <- ties[1,2] <- 9
# Now fit the model
fit <- vglm(Brat(mat, ties) ~ 1, bratt(refgp = 1), trace = TRUE,
crit = "coef")
summary(fit)
c(0, coef(fit)) # Log-abilities (last is log(alpha0))
c(1, Coef(fit)) # Abilities (last is alpha0)
fit@misc$alpha # alpha_1,...,alpha_M
fit@misc$alpha0 # alpha_0
fitted(fit) # Probabilities of winning and tying, in awkward form
predict(fit)
(check <- InverseBrat(fitted(fit))) # Probabilities of winning
qprob <- attr(fitted(fit), "probtie") # Probabilities of a tie
qprobmat <- InverseBrat(c(qprob), NCo = nrow(ties)) # Pr(tie)
check + t(check) + qprobmat # Should be 1s in the off-diagonals
Run the code above in your browser using DataLab