SHELF (version 1.2.0)

fitdist: Fit distributions to elicited probabilities

Description

Takes elicited probabilities as inputs, and fits parametric distributions using least squares on the cumulative distribution function. If separate judgements from multiple experts are specified, the function will fit one set of distributions per expert.

Usage

fitdist(vals, probs, lower = -Inf, upper = Inf, weights = 1, tdf = 3)

Arguments

vals

A vector of elicited values for one expert, or a matrix of elicited values for multiple experts (one column per expert). Note that the an elicited judgement about X should be of the form P(X

probs

A vector of elicited probabilies for one expert, or a matrix of elicited values for multiple experts (one column per expert). A single vector can be used if the probabilities are the same for each expert. For each expert, the smallest elicited probability must be less than 0.4, and the largest elicited probability must be greater than 0.6.

lower

A single lower limit for the uncertain quantity X, or a vector of different lower limits for each expert. Specifying a lower limit will allow the fitting of distributions bounded below.

upper

A single upper limit for the uncertain quantity X, or a vector of different lower limits for each expert. Specifying both a lower limit and an upper limit will allow the fitting of a Beta distribution.

weights

A vector or matrix of weights corresponding to vals if weighted least squares is to be used in the parameter fitting.

tdf

The number of degrees of freedom to be used when fitting a t-distribution.

Value

An object of class elicitation. This is a list containing the elements . This is a list containing the elements

Examples

## Not run: 
# # One expert, with elicited probabilities
# # P(X<20)=0.25, P(X<30)=0.5, P(X<50)=0.75
# # and X>0.
# v <- c(20,30,50)
# p <- c(0.25,0.5,0.75)
# fitdist(vals=v, probs=p, lower=0)
# 
# # Now add a second expert, with elicited probabilities
# # P(X<55)=0.25, P(X<60=0.5), P(X<70)=0.75
# v <- matrix(c(20,30,50,55,60,70),3,2)
# p <- c(0.25,0.5,0.75)
# fitdist(vals=v, probs=p, lower=0)
# 
# # Two experts, different elicited quantiles and limits.
# # Expert 1: P(X<50)=0.25, P(X<60=0.5), P(X<65)=0.75, and provides bounds 10<X<100
# # Expert 2: P(X<40)=0.33, P(X<50=0.5), P(X<60)=0.66, and provides bounds 0<X
# v <- matrix(c(50,60,65,40,50,60),3,2)
# p <- matrix(c(.25,.5,.75,.33,.5,.66),3,2)
# l <- c(10,0)
# u <- c(100, Inf)
# fitdist(vals=v, probs=p, lower=l, upper=u)
# ## End(Not run)