SHELF (version 1.2.0)

SHELF-package: Tools to Support the Sheffield Elicitation Framework

Description

Implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability or quantile judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of additional quantiles. A graphical interface for the roulette elicitation method is also provided. For multiple experts, a weighted linear pool can be calculated.

Package:

SHELF

Type:

Package

Version:

1.2.0

Date:

2016-08-16

License:

GPL-2 | GPL-3

References

The SHELF homepage

Examples

## Not run: 
# ## 1) Elicit judgements from two experts individually 
# # Expert 1 states P(X<30)=0.25, P(X<40)=0.5, P(X<50)=0.75
# # Expert 2 states P(X<20)=0.25, P(X<25)=0.5, P(X<35)=0.75
# # Both experts state 0<X<100.
# 
# ## 2) Fit distributions to each expert's judgements
# v <- matrix(c(30, 40, 50, 20, 25, 35), 3, 2)
# p <- c(0.25, 0.5, 0.75)
# myfit <- fitdist(vals = v, probs = p, lower = 0, upper = 100)
# 
# ## 3) Plot the fitted distributions, including a linear pool
# plotfit(myfit, lp = T)
# 
# ## 4) Now elicit a single 'consensus' distribution from the two experts
# # Suppose they agree P(X<25)=0.25, P(X<30)=0.5, P(X<40)=0.75
# v <-c(25, 30, 40)
# p <-c(0.25, 0.5, 0.75)
# myfit <- fitdist(vals = v, probs = p, lower = 0, upper = 100)
# 
# ## 5) Plot the fitted density, and report some feedback, such as the 
# # fitted 5th and 95th percentiles
# plotfit(myfit, ql = 0.05, qu = 0.95)
# feedback(myfit, quantiles = c(0.05, 0.95))
# 
# ## Can also use interactive plotting
# v <- matrix(c(30, 40, 50, 20, 25, 35), 3, 2)
# p <- c(0.25, 0.5, 0.75)
# myfit <- fitdist(vals = v, probs = p, lower = 0, upper = 100)
# # plot each distribution
# plotfit(myfit, int = TRUE)
# 
# ## plot the distribution for one expert only
# plotfit(myfit, int = TRUE, ex = 1)
# 
# ## Enter judgements in interactive mode
# elicit()
# 
# ## Enter judgements using the roulette method
# roulette(Lo = 0, Up = 100, nbins = 10, gridheight = 10)
# ## End(Not run)