reduction

aplRDV

This utility function is used to obtain a summary of a vector of data for many operators. The function is taken from the project APL in R (<a href="https://rpubs.com/deleeuw/158476">https://rpubs.com/deleeuw/158476</a>).

Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.

Peiyuan Zhu

Using the Theory of Belief Functions

Claude Boivin

reduction function

<dl><dt>x</dt>
<dd>A vector of numbers or a character string.</dd>
<dt>f</dt>
<dd>The operator. Must be compatible with the type of input vector (numeric or character)</dd></dl>

Arguments

Author

This utility function is used to obtain a summary of a vector of data for many operators. The function is taken from the project APL in R (<a href='https://rpubs.com/deleeuw/158476'>https://rpubs.com/deleeuw/158476</a>).

Summary of a vector for any operator. — reduction

<dl>

<dt>x</dt>
<dd>A vector of numbers or a character string.</dd>


<dt>f</dt>
<dd>The operator. Must be compatible with the type of input vector (numeric or character)</dd>

</dl>

reduction: Summary of a vector for any operator.

Description

Usage

Value

Arguments

Author

References

Examples