sort.summary.eviSimulation

Sort summarized EVI simulation results according to their EVI.

Supporting the quantitative analysis of binary welfare based
decision making processes using Monte Carlo simulations. Decision support
is given on two levels: (i) The actual decision level is to choose between
two alternatives under probabilistic uncertainty. This package calculates
the optimal decision based on maximizing expected welfare. (ii) The meta
decision level is to allocate resources to reduce the uncertainty in the
underlying decision problem, i.e to increase the current information to
improve the actual decision making process. This problem is dealt with
using the Value of Information Analysis. The Expected Value of
Information for arbitrary prospective estimates can be calculated as
well as Individual Expected Value of Perfect Information.
The probabilistic calculations are done via Monte Carlo
simulations. This Monte Carlo functionality can be used on its own.

Eike Luedeling

decisionSupport

Quantitative Support of Decision Making under Uncertainty

Lutz Goehring

Katja Schiffers

Cory Whitney

Eduardo Fernandez

sort.summary.eviSimulation function

<dl><dt>x</dt>
<dd>An object of class <code>summary.eviSimulation</code>.</dd>
<dt>decreasing</dt>
<dd><code>logical</code>: if the EVI should be sorted in decreasing order.</dd>
<dt>...</dt>
<dd>currently not used</dd>
<dt>along</dt>
<dd><code>character</code>: the name of the valuation variable along which the EVI 
should be sorted.</dd></dl>

Arguments

Sort Summarized EVI Simulation Results.. — sort.summary.eviSimulation

<dl>

<dt>x</dt>
<dd>An object of class <code>summary.eviSimulation</code>.</dd>


<dt>decreasing</dt>
<dd><code>logical</code>: if the EVI should be sorted in decreasing order.</dd>


<dt>...</dt>
<dd>currently not used</dd>


<dt>along</dt>
<dd><code>character</code>: the name of the valuation variable along which the EVI 
should be sorted.</dd>

</dl>

sort.summary.eviSimulation: Sort Summarized EVI Simulation Results..

Description

Usage

Value

Arguments

See Also