betaExpert | R Documentation |
The betaExpert
function fits a (standard) Beta distribution to expert opinion. The expert provides information on a best-guess estimate (mode or mean), and an uncertainty range:
The parameter value is with 100*p%
certainty greater than lower
The parameter value is with 100*p%
certainty smaller than upper
The parameter value lies with 100*p%
in between lower
and upper
betaExpert(best, lower, upper, p = 0.95, method = "mode") ## S3 method for class 'betaExpert' print(x, conf.level = .95, ...) ## S3 method for class 'betaExpert' plot(x, y, ...)
best |
Best-guess estimate; see argument |
lower |
Lower uncertainty limit |
upper |
Upper uncertainty limit |
p |
Expert's certainty level |
method |
Does best-guess estimate correspond to the |
x |
Object of class |
y |
Currently not implemented |
conf.level |
Confidence level used in printing quantiles of resulting Beta distribution |
... |
Other arguments to pass to function |
The methodology behind the betaExpert
function is presented by Branscum et al. (2005) and implemented in the BetaBuster software, written by Chun-Lung Su.
The parameters of a standard Beta distribution are calculated based on a best-guess estimate and a 100(p)% uncertainty range, defined by a lower and/or upper limit. The betaExpert
function uses minimization (optimize
) to derive α and β from this best guess and lower and/or upper limit. The resulting distribution is a standard 2-parameter Beta distribution: Beta(α, β).
A list of class "betaExpert"
:
alpha |
Parameter α (shape1) of the Beta distribution |
beta |
Parameter β (shape2) of the Beta distribution |
The print
method for "betaExpert"
additionally calculates the mean, median, mode, variance and range of the corresponding Beta distribution.
Brecht Devleesschauwer <brechtdv@gmail.com>
Branscum AJ, Gardner IA, Johnson WO (2005) Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling. Prev Vet Med 68:145-163.
Package rriskDistributions, which provides a collection of functions for fitting distributions to given data or by known quantiles.
betaPERT
, for modelling a generalized Beta distribution based on expert opinion
## Most likely value (mode) is 90% ## Expert states with 95% certainty that true value is larger than 70% betaExpert(best = 0.90, lower = 0.70, p = 0.95) ## Most likely value (mode) is 0% ## Expert states with 95% certainty that true value is smaller than 40% betaExpert(best = 0, upper = 0.40, p = 0.95) ## Most likely value (mode) is 80% ## Expert states with 90% certainty that true value lies in between 40% and 90% betaExpert(best = 0.80, lower = 0.40, upper = 0.90, p = 0.90) ## Mean value is assumed to be 80% ## Expert states with 90% certainty that true value lies in between 40% and 90% betaExpert(best = 0.80, lower = 0.40, upper = 0.90, p = 0.90, method = "mean")
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