elicit_roulette: Roulette-method elicitation (chip-allocation)

In bayprior: Bayesian Prior Elicitation and Diagnostics for Clinical Trials

Roulette-method elicitation (chip-allocation)

Description

Implements the SHELF roulette method: the expert allocates a fixed number of "chips" across a set of pre-defined bins representing the range of the quantity. The resulting histogram is fitted to a parametric distribution.

Usage

elicit_roulette(
  chips,
  breaks,
  family = c("beta", "normal", "gamma", "lognormal"),
  expert_id = "Expert_1",
  label = "Unknown quantity"
)

Arguments

chips	Integer vector. Number of chips in each bin (left-to-right).
breaks	Numeric vector of length length(chips) + 1 defining the bin edges.
family	Character. Distribution to fit. One of "beta", "normal", "gamma", "lognormal".
expert_id	Character. Expert identifier.
label	Character. Quantity description.

Details

In the Shiny app (⁠Prior elicitation tab⁠) the roulette grid is rendered interactively. This function provides the fitting back-end that can also be called programmatically when chips are known.

Chips are converted to relative frequencies, and bin midpoints are used as representative values. The chosen family is then fitted by minimising the weighted sum of squared CDF differences (a histogram-matching approach).

Value

A bayprior object fitted to the chip histogram.

References

Oakley, J. E. & O'Hagan, A. (2010). SHELF: the Sheffield Elicitation Framework. University of Sheffield.

Examples

# Expert places 0, 2, 5, 8, 5, 2, 1 chips across bins [0,.1,.2,...,.7]
prior <- elicit_roulette(
  chips   = c(0L, 2L, 5L, 8L, 5L, 2L, 1L),
  breaks  = seq(0, 0.7, by = 0.1),
  family  = "beta",
  label   = "Response rate"
)
print(prior)

bayprior documentation built on Aug. 27, 2026, 1:09 a.m.