plinearpool: Probabilities quantiles and samples from a (weighted) linear...

In SHELF: Tools to Support the Sheffield Elicitation Framework

Probabilities quantiles and samples 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, or generates a random sample.

Usage

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

Arguments

fit	The output of a fitdist command.
x	A vector of required cumulative probabilities P(X<=x)
d	Scalar or vector of distributions to use for each expert. Options for each vector element are "hist", "normal", "t", "gamma", "lognormal", "logt","beta", "best". If given as a scalar, same choice is used for all experts.
w	A vector of weights to be used in the weighted linear pool.
q	A vector of required quantiles
n	Number of random samples from the linear pool

Details

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

Value

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

Author(s)

Jeremy Oakley <[e-mail]>

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)

SHELF documentation built on Sept. 11, 2024, 6:54 p.m.