random_state

This function draws a sample from a user-defined frequency distribution for a categorical
variable.

probability

"conditional

"random

CPTs

state"

table"

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

random_state function

<dl><dt>states</dt>
<dd>character vector containing state names.</dd>
<dt>probs</dt>
<dd>numeric vector containing probabilities for the states. If these do
not add up to 1, they are automatically normalized.</dd></dl>

Arguments

Author

Draw a random state for a categorical variable — random_state

<dl>

<dt>states</dt>
<dd>character vector containing state names.</dd>


<dt>probs</dt>
<dd>numeric vector containing probabilities for the states. If these do
not add up to 1, they are automatically normalized.</dd>

</dl>

Draw a random state for a categorical variable

random_state: Draw a random state for a categorical variable

Description

Usage

Value

Arguments

Author

Examples