regret {gosset}

Regret-based values for risk assessment

Description

Regret is an important heuristic in the behavioural sciences. Minimizing worst regret (the loss under the worst possible outcome) is a criterion that takes a conservative approach to risk analysis in diversification strategies.

Usage

regret(object, ..., bootstrap = TRUE, normalize = TRUE)

## Default S3 method:
regret(object, ..., values, items, group, bootstrap = TRUE, normalize = TRUE)

## S3 method for class 'pltree'
regret(object, bootstrap = TRUE, normalize = TRUE, ...)

## S3 method for class 'list'
regret(object, bootstrap = TRUE, normalize = TRUE, ...)

Arguments

object

a data.frame, an object of class pltree, or a list with PlackettLuce models

...

further arguments passed to methods

bootstrap

logical, to run a Bayesian bootstrap on object

normalize

logical, to normalize values to sum to 1

values

an index in object with the values to compute regret

items

an index in object for the different items

group

an index in object for the different scenarios

Details

Additional details for Bayesian bootstrap: statistic A function that accepts data as its first argument and possibly, the weights as its second, if use_weights is TRUE; n1 The size of the bootstrap sample; n2 The sample size used to calculate the statistic each bootstrap draw

Value

A data frame with regret estimates

items

the item names

worth

the worth parameters

regret

the squared regret

worst_regret

the worst regret

Author(s)

Jacob van Etten and Kauê de Sousa

References

Loomes G. & Sugden R. (1982). The Economic Journal, 92(368), 805. doi:10.2307/2232669

Bleichrodt H. & Wakker P. P. (2015). The Economic Journal, 125(583), 493–532. doi:10.1111/ecoj.12200

Examples

# Case 1 ####
library("PlackettLuce")
data("breadwheat", package = "gosset")

# convert the tricot rankings from breadwheat data
# into a object of class 'grouped_rankings'

G = rank_tricot(breadwheat,
                 items = c("variety_a","variety_b","variety_c"),
                 input = c("overall_best","overall_worst"),
                 group = TRUE)


# combine grouped rankings with temperature indices
mydata = cbind(G, breadwheat[c("lon","lat")])

# fit a pltree model using geographic data
mod = pltree(G ~ ., data = mydata)

regret(mod)

# Case 2 ####
# list of PlackettLuce models
R = matrix(c(1, 2, 3, 0,
              4, 1, 2, 3,
              2, 1, 3, 4,
              1, 2, 3, 0,
              2, 1, 3, 0,
              1, 0, 3, 2), nrow = 6, byrow = TRUE)
colnames(R) = c("apple", "banana", "orange", "pear")

mod1 = PlackettLuce(R)

R2 = matrix(c(1, 2, 0, 3,
               2, 1, 0, 3,
               2, 1, 0, 3,
               1, 2, 0, 3,
               2, 1, 0, 3,
               1, 3, 4, 2), nrow = 6, byrow = TRUE)
colnames(R2) = c("apple", "banana", "orange", "pear")

mod2 = PlackettLuce(R2)

mod = list(mod1, mod2)

regret(mod, n1 = 500)