fitprecision: Fit a distribution to judgements about a population precision

Description Usage Arguments Details Value Examples

View source: R/fitprecision.R

Description

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.

Usage

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fitprecision(
  interval,
  propvals,
  propprobs = c(0.05, 0.95),
  med = interval[1],
  trans = "identity",
  pplot = TRUE,
  tdf = 3,
  fontsize = 12
)

Arguments

interval

A vector specifying the endpoints of an interval [k_1, k_2].

propvals

A vector specifying two values θ_1, θ_2 for the proportion.

propprobs

A vector specifying two probabilities p_1, p_2.

med

The hypothetical value of the population median.

trans

A string variable taking the value "identity", "log" or "logit" corresponding to whether the population distribution is normal, lognormal or logit-normal respectively.

pplot

Plot the population distributions with median set at k_1 and precision fixed at the two elicited quantiles implied by propvals and propprobs.

tdf

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

fontsize

Font size used in the plots.

Details

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.

Value

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- lower) - location) / scale has a standard t distribution. The degrees of freedom is not fitted: it is specified as an input argument.

vals

The elicited values θ_1, θ_2

probs

The elicited probabilities p_1, p_2

limits

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

transform

Transformation used for a normal population distribution.

Examples

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## Not run: 
fitprecision(interval=c(60, 70), propvals=c(0.2, 0.4), trans = "log")
  
## End(Not run)

OakleyJ/SHELF documentation built on Feb. 14, 2020, 8:17 a.m.