Build Plackett-Luce rankings from tricot dataset

Create an object of class "rankings" from tricot data

rankTricot(
  data,
  items,
  input,
  group = FALSE,
  validate.rankings = FALSE,
  additional.rank = NULL,
  ...
)

Arguments

data

a data.frame with columns specified by items and input values

items

a character or numerical vector for indexing the column(s) containing the item names in data

input

a character or numerical vector for indexing the column(s) containing the values in data to be ranked

group

logical, if TRUE return an object of class "grouped_rankings"

validate.rankings

logical, if TRUE implements a check on ranking consistency looking for possible ties, NA or letters other than A, B, C. These entries are set to 0

additional.rank

optional, a data frame for the comparisons between tricot items and the local item

...

additional arguments passed to methods. See details

Value

a PlackettLuce "rankings" or "grouped_rankings" object

Details

full.output: logical, to return a list with a "rankings", a "grouped_rankings" and the ordered items

References

van Etten J., et al. (2019). Experimental Agriculture, 55(S1), 275–296. doi:10.1017/S0014479716000739

Author

Kauê de Sousa and Jacob van Etten, with ideas from Heather Turner

Examples

if (FALSE) { # interactive()
# beans data where each observer compares 3 varieties randomly distributed
# from a list of 11 and additionally compares these 3 varieties
# with their local variety
if (require("PlackettLuce")){
  data("beans", package = "PlackettLuce")
  
  # first build rankings with only tricot items
  # and return an object of class 'rankings'
  R = rankTricot(data = beans,
                  items = c(1:3),
                  input = c(4:5))
  head(R)
  
  ############################################################
  
  # pass the comparison with local item as an additional rankings, then
  # each of the 3 varieties are compared separately with the local item
  # and return an object of class grouped_rankings
  G = rankTricot(data = beans,
                  items = c(1:3),
                  input = c(4:5),
                  group = TRUE,
                  additional.rank = beans[c(6:8)])
  
  head(G)
}
}