mV: Helper function that translates elicited quantiles of target...

In indirect: Elicitation of Independent Conditional Means Priors for Generalised Linear Models

Description

The default for fit.method is option KL. This option uses an objective function that minimises a discretised directed divergence from a cumulative distribution implied by raw elicited fractiles to a normal conditional mean prior for the linear predictor. An alterative method moment assigns the location parameter of the normal conditional mean prior to the elicited median on the linear predictor scale. The variance parameter is estimated as V = ((g(f_u) - g(f_l)/(qnorm(u) - qnorm(l)))^2, where l is the probability associated with the fractile f_l that defines the lower bound for the central credible interval and u is the probability associated with the fractile f_u that defines the upper bound for the central credible interval. This is also used to initialise the optimisation for the KL method. Another optimsation method that minimises the sum of squares is also available as method SS. See the vignette for more details on the choice of objective function for KL and SS.

Usage

mV(Z, fit.method = "KL")

Arguments

Z	list object that contains matrix theta of elicitations and character link, see plotDesignPoint
fit.method	character, moment, KL, SS. Default is KL.

Value

A list with vector of means m and diagonal covariance matrix V.

indirect documentation built on Feb. 9, 2022, 9:07 a.m.