compute_model

Evaluation of decision tree nodes. All the MAUT model is computed at every level 
the utilities are computed considering the given weights.

Provides functions for the creation, evaluation and test of decision models based in
Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities
for a set of indexes, compute utilities and weights for the whole decision tree defining the
decision model and simulate weights employing Dirichlet distributions under addition constraints
in weights.
Also includes other rating analysis methods as for example the Colley, Offensive - Defensive
ratings and the ranking aggregation with Borda count.

Pedro Guarderas

Decision Models with Multi Attribute Utility Theory

Felipe Aguirre

Julio Andrade

Daniel Lagos

Andrés Lopez

Nelson Recalde

Edison Salazar

compute_model function

<dl><dt>tree</dt>
<dd>initial tree structure with utilities in its leafs.</dd>
<dt>utilities</dt>
<dd>data.table with ordered columns containing the values of utilities.</dd>
<dt>weights</dt>
<dd>weights for the decision model.</dd></dl>

Arguments

Pedro Guarderas, Andrés Lopez
<a href="/link/pedro.felipe.guarderas%40gmail.com?package=mau&version=0.4.0" data-mini-rdoc="mau::pedro.felipe.guarderas@gmail.com">pedro.felipe.guarderas@gmail.com</a>

Author

Evaluation of decision tree nodes — compute_model

<dl>

<dt>tree</dt>
<dd>initial tree structure with utilities in its leafs.</dd>


<dt>utilities</dt>
<dd>data.table with ordered columns containing the values of utilities.</dd>


<dt>weights</dt>
<dd>weights for the decision model.</dd>

</dl>

Pedro Guarderas, Andrés Lopez
<a href='mailto:pedro.felipe.guarderas@gmail.com'>pedro.felipe.guarderas@gmail.com</a>

compute_model: Evaluation of decision tree nodes

Description

Usage

Value

Arguments

Author

Details

See Also

Examples