summary.welfareDecisionAnalysis

An object of class <code>welfareDecisionAnalysis</code>.

object

Further arguments passed to <code><a rd-options="" href="/link/format?package=decisionSupport&version=1.103.8" data-mini-rdoc="decisionSupport::format">format</a></code>.

how many significant digits are to be used for
 numeric and complex <code>x</code>. The default, <code>NULL</code>, uses
 <code><a rd-options="" href="/link/getOption?package=decisionSupport&version=1.103.8" data-mini-rdoc="decisionSupport::getOption">getOption</a>("digits")</code>. This is a suggestion: enough decimal
 places will be used so that the smallest (in magnitude) number has
 this many significant digits, and also to satisfy <code>nsmall</code>.
 (For the interpretation for complex numbers see <code><a rd-options="" href="/link/signif?package=decisionSupport&version=1.103.8" data-mini-rdoc="decisionSupport::signif">signif</a></code>.)

digits

<code>numeric vector</code>: quantiles that shall be displayed; if <code>=NULL</code> no 
quantiles will be displayed.

probs

Produce a summary of the results of a welfare decision analysis obtained by the function
 <code><a rd-options="" href="/link/welfareDecisionAnalysis?package=decisionSupport&version=1.103.8" data-mini-rdoc="decisionSupport::welfareDecisionAnalysis">welfareDecisionAnalysis</a></code>.

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

World Agroforesrtry Centre (ICRAF) 

summary.welfareDecisionAnalysis function

Further arguments passed to <code><a rd-options='' href='format'>format</a></code>.

how many significant digits are to be used for
 numeric and complex <code>x</code>. The default, <code>NULL</code>, uses
 <code><a rd-options='' href='getOption'>getOption</a>("digits")</code>. This is a suggestion: enough decimal
 places will be used so that the smallest (in magnitude) number has
 this many significant digits, and also to satisfy <code>nsmall</code>.
 (For the interpretation for complex numbers see <code><a rd-options='' href='signif'>signif</a></code>.)

Produce a summary of the results of a welfare decision analysis obtained by the function
 <code><a rd-options='' href='welfareDecisionAnalysis'>welfareDecisionAnalysis</a></code>.

summary.welfareDecisionAnalysis: Summarize Welfare Decision Analysis results.

Description

Usage

Arguments

Value

See Also