SHELF (version 1.2.0)

plinearpool: Calculate fitted probabilities or quantiles from a (weighted) linear pool

Description

Calculates a linear pool given a set of elicited judgements in a fit object. Then calculates required probabilities or quantiles from the pooled cumulative distribution function.

Usage

plinearpool(fit, x, d = "best", w = 1)
qlinearpool(fit, q, d = "best", w = 1)

Arguments

fit

The output of a fitdist command.

x

A vector of required cumulative probabilities P(X

d

The distribution fitted to each expert's probabilities. This must either be the same distribution for each expert, or the best fitting distribution for each expert. Options are "normal", "t", "gamma", "lognormal", "logt","beta", "best".

w

A vector of weights to be used in the weighted linear pool.

q

A vector of required quantiles

Value

A probability or quantile, calculate from a (weighted) linear pool (arithmetic mean) of the experts' individual fitted probability.

Details

Quantiles are calculate by first calculating the pooled cumulative distribution function at 100 points, and then using linear interpolation to invert the CDF.

Examples

## Not run: 
# # 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.
# 
# 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)
# 
# plinearpool(myfit, x=c(20, 50, 80))
# qlinearpool(myfit, q=c(0.05, 0.5, 0.95))
# 
# # give more weight to first expert
# plinearpool(myfit, x=c(20, 50, 80), w=c(0.7, 0.3)) 
# 
# # force the use of gamma distributions for each expert
# qlinearpool(myfit, q=c(0.05, 0.5, 0.95), d="gamma") 
# ## End(Not run)