vignettes/agRank_vignettes.R

In shuxiaoc/agRank: Rank Aggregation in Phenotypic Selection

## ------------------------------------------------------------------------
suppressMessages(library(agRank))
library(doParallel)
#draw a score vector
score = runif(120, 10, 20)
#the variance is set to be 1
Svar = rep(1, 120)

ranks_full = foreach(i = 1:240, .combine = 'rbind') %do% {
  
  rThurstone(score, Svar)$ranks
  
}

#assign labels to varieties
colnames(ranks_full) = 1:120

dim(ranks_full) #240 * 120 matrix, each row is the ranking given by that farmer


## ------------------------------------------------------------------------
design = expDesign(120, 240, method = 'random')
ranks_partial = matrix(0, 240, 120) #the partial ranking, varieties not compared are set to 0
colnames(ranks_partial) = 1:120
for(k in 1:240){
      
      ranks_full[k, ][-design[k, ]] = 0 #varieties not ranked are set to 0
      ranks_temp = ranks_full[k, ][ranks_full[k, ] != 0] #ranks of varieties included
      sorted_ranks = sort(ranks_temp)
      label = as.numeric(names(sorted_ranks)) #ranking in the form label[1] \succ label[2] \succ ...
      ranks_partial[k, ][label] = c(1,2,3) #assign ranks 1,2,3
}

#now look at the ranks given by the first farmer
ranks_partial[1, ]

## ------------------------------------------------------------------------
#first we use the identity matrix as the relation ship matrix, for simplicity
K = diag(1, 120)
###using method = 'LM'
ranks_LM = rankAg(ranks_partial, K, method = 'LM')$ranks

###using method = 'PL'
ranks_PL = rankAg(ranks_partial, K, method = 'PL')$ranks

###recall the real ranks
ranking_real = order(score, decreasing = TRUE) #the ranking in the form of A > B > C...
ranks_real = match(1:120, ranking_real) #the ranks

#calculate the Kendall's correlation coefficient
cor(ranks_LM, ranks_real, method = 'kendall')
cor(ranks_PL, ranks_real, method = 'kendall')