rank_tricot | R Documentation |
Create an object of class "rankings" from tricot data. Tricot stands for "triadic comparison of technology options". Is an approach to carry out large decentralized agronomic field experiments as incomplete blocks. Each incomplete block contains a set of three randomised technologies out of a larger set.
rank_tricot(
data,
items,
input,
group = FALSE,
validate.rankings = FALSE,
additional.rank = NULL,
...
)
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 |
input |
a character or numerical vector for indexing the column(s)
containing the values in |
group |
logical, if |
validate.rankings |
logical, if |
additional.rank |
optional, a data frame for the comparisons between tricot items and the local item |
... |
additional arguments passed to methods. See details |
full.output: logical, to return a list with a "rankings", a "grouped_rankings" and the ordered items
a PlackettLuce "rankings" object, which is a matrix of dense rankings
Kauê de Sousa and Jacob van Etten, with ideas from Heather Turner
van Etten J., et al. (2016). Experimental Agriculture, 55(S1), 275–296. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1017/S0014479716000739")}
rankings
, breadwheat
Other rank functions:
rank_numeric()
,
set_binomialfreq()
,
set_paircomp()
library("PlackettLuce")
data("beans", package = "PlackettLuce")
# Using a subset of the bean data
beans = beans[1:5, 1:5]
beans[1, 1] = NA
beans[3, 4:5] = NA
beans[5, 5] = "Tie"
# The default approach do not validate rankings
# accepting any entry used in the argument input
R1 = rank_tricot(beans,
items = c(1:3),
input = c(4:5),
group = FALSE)
# Using validate.rankings = TRUE, the rankings
# are only considered for those entries without
# NAs, Ties and with any of the letters A, B, C
# this do not affect the lenght of the final ranking
R2 = rank_tricot(beans,
items = c(1:3),
input = c(4:5),
validate.rankings = TRUE,
group = FALSE)
coef(PlackettLuce(R1))
coef(PlackettLuce(R2))
############################################################
# 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
data("beans", package = "PlackettLuce")
G = rank_tricot(data = beans,
items = c(1:3),
input = c(4:5),
group = TRUE,
additional.rank = beans[c(6:8)])
head(G)
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