ClimMobTools (version 1.4)

rankTricot: Build Plackett-Luce rankings from tricot dataset

Description

Create an object of class "rankings" from tricot data

Usage

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

Value

a PlackettLuce "rankings" or "grouped_rankings" object

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

Author

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

Details

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

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)
}
}