quantify_uncertainty_pp_change_eke: Fit a Normal distribution from Expert Kwonledge elicitation

In Iraices/PrecisePvsBoundedP: A suggestion for the quantification of precise and bounded probability to express epistemic uncertainty in scientific assessments

Description

Takes elicited probabilities as inputs, and fits a normal distribution 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

quantify_uncertainty_pp_change_eke(vals, probs)

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<= vals[i,j]) = probs[i,j]
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.

Value

A vector with the parameters mean (mu_EKE) and standard deviation (sigma_EKE) of the fitted normal distribution.

Examples

vals = c(-30, -15, 05, 7.5, 20)
probs = c(0.01, 0.25,0.5, 0.75,0.99)
quantify_uncertainty_pp_change_eke(vals, probs)

Iraices/PrecisePvsBoundedP documentation built on Jan. 18, 2021, 11:32 p.m.