paramtnormci_numeric

<code>numeric</code> 2-dimensional vector; probabilities of lower and upper bound of the
corresponding confidence interval.

<code>numeric</code> 2-dimensional vector; lower, i.e <code>ci[[1]]</code>, and upper bound, i.e
<code>ci[[2]]</code>, of the confidence interval.

<code>numeric</code>; lower truncation point of the distribution (&gt;= <code>-Inf</code>).

lowerTrunc

<code>numeric</code>; upper truncation point of the distribution (&lt;= <code>Inf</code>).

upperTrunc

<code>numeric</code>; the relative tolerance level of deviation of the
generated confidence interval from the specified interval. If this deviation is greater than
<code>relativeTolerance</code> a warning is given.

relativeTolerance

<code>character</code>; if <code>="probability"</code> the equation defining the parameters <code>mean</code> and
<code>sd</code> is the difference between calculated and given probabilities of the confidence
interval; if <code>="quantile"</code> the equation defining the parameters is the difference between
calculated and given upper and lower value of the confidence interval.

rootMethod

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

This function calculates the distribution parameters, i.e. <code>mean</code> and <code>sd</code>, of a
truncated normal distribution from an arbitrary confidence interval.

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) 

paramtnormci_numeric function

Further parameters passed to <code><a rd-options='nleqslv' href='nleqslv'>nleqslv</a></code>.

paramtnormci_numeric: Return parameters of truncated normal distribution based on a confidence interval.

Description

Usage

Arguments

Value

Details

See Also