process.elidat: Process the elicited data to an optimised _Density Ratio...

Description Usage Arguments Details Value Author(s) References See Also

Description

Constructs the smallest Density Ratio Class for elicited probability-quantile points (or intervals) given a lower and upper distributional shape. Used optimisation algorithms are the methods Nelder-Mead and L-BFGS-B implemented in the standard R function optim.

Usage

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process.elidat(p = p, q = q, dist.lower, dist.upper, 
               start.dist.lower.par = NA, start.dist.upper.par = NA, ...)

Arguments

p

Vector of probabilities in the according order to q.

q

Vector of quantiles in the according order to p.

dist.lower

Lower distribution as an element of the class distribution.

dist.upper

Upper distribution as an element of the class distribution.

start.dist.lower.par

Start values of the parameters of the lower distribution that shall be optimized.

start.dist.upper.par

Start values of the parameters of the upper distribution that shall be optimized.

...

Details

Only the specified start values of the lower and upper distribution are optimised. If no optimisation shall be executed (fixed parameters of the distributions) then use calc.k.

Value

drclass

an object of the class drclass.

Author(s)

Simon L. Rinderknecht

References

Rinderknecht, S.L., Borsuk, M.E. and Reichert, P. Eliciting Density Ratio Classes. International Journal of Approximate Reasoning 52, 792-804, 2011. doi10.1016/j.ijar.2011.02.002. \ Rinderknecht, S. L., Borsuk, M. E. and Reichert, P. Bridging Uncertain and Ambiguous Knowledge with Imprecise Probabilities, Environmental Modelling & Software 36, 122-130, 2012.

See Also

See also fitDRC, distribution, transformation, dist.trans.create.



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