# plinearpool: Calculate fitted probabilities or quantiles from a (weighted)... In SHELF: Tools to Support the Sheffield Elicitation Framework

## 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

 ```1 2``` ```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<=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

## 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 <[email protected]>

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```## 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

SHELF documentation built on Nov. 17, 2017, 4:52 a.m.