Description Usage Arguments Details Value Examples

Takes elicited probabilities about proportion of a population lying in a specfied interval as inputs, converts the judgements into probability judgements about the population precision, and fits gamma and lognormal distributions to these judgements using the fitdist function.

1 2 3 4 5 6 7 8 9 10 | ```
fitprecision(
interval,
propvals,
propprobs = c(0.05, 0.95),
med = interval[1],
trans = "identity",
pplot = TRUE,
tdf = 3,
fontsize = 12
)
``` |

`interval` |
A vector specifying the endpoints of an interval |

`propvals` |
A vector specifying two values |

`propprobs` |
A vector specifying two probabilities |

`med` |
The hypothetical value of the population median. |

`trans` |
A string variable taking the value |

`pplot` |
Plot the population distributions with median set at |

`tdf` |
Degrees of freedom in the fitted log Student-t distribution. |

`fontsize` |
Font size used in the plots. |

The expert provides a pair of probability judgements

*P(θ < θ_1 ) = p_1,*

and

*P(θ < θ_2) = p_2,*

where *θ* is the proportion of the population that lies in the interval
*[k_1, k_2]*, conditional on the population median taking some hypothetical value (*k_1*
by default). *k_1* can be set to `-Inf`

, or *k_2* can be set to `Inf`

;
in either case, the hypothetical median value must be specified. If both *k_1*
and *k_2* are finite, the hypothetical median must be one of the interval endpoints.
Note that, unlike the fitdist command, a 'best fitting'
distribution is not reported, as the distributions are fitted to two elicited
probabilities only.

`Gamma` |
Parameters of the fitted gamma distribution. Note that E(precision) = shape / rate. |

`Log.normal` |
Parameters of the fitted log normal distribution: the mean and standard deviation of log precision. |

`Log.Student.t` |
Parameters of the fitted log student t distributions.
Note that (log(X- |

`vals` |
The elicited values |

`probs` |
The elicited probabilities |

`limits` |
The lower and upper limits specified by each expert (+/- Inf if not specified). |

`transform` |
Transformation used for a normal population distribution. |

1 2 3 4 | ```
## Not run:
fitprecision(interval=c(60, 70), propvals=c(0.2, 0.4), trans = "log")
## End(Not run)
``` |

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