setmapmi

Creates a set map visualization from the output of [pairmi()],
showing which original variables compose the derived sets at a specified
depth.

Create sets of variables based on a mutual
information approach. In this context, a set is a collection of
distinct elements (e.g., variables) that can also be treated as a
single entity.  Mutual information, a concept from probability theory,
quantifies the dependence between two variables by expressing how much
information about one variable can be gained from observing the other.
Furthermore, you can analyze, and visualize
these sets in order to better understand the relationships among
variables.

Nicolas Leenaerts

setweaver

Building Sets of Variables in a Probabilistic Framework

Aaron Fisher

setmapmi function

<dl><dt>original_variables</dt>
<dd>Character vector of names for the original
variables that were paired (typically `pairmi_result$original.variables`).</dd>
<dt>sets</dt>
<dd>A data frame returned by [pairmi()] describing the sets. Must
contain the columns required by `setmapmi()` (e.g., identifiers for sets
and their constituent variables).</dd>
<dt>n_elements</dt>
<dd>Integer scalar giving the set size (depth) to visualize
(e.g., `2` for pairs, `3` for triplets). Must be &gt;= 1 and present in
`sets`.</dd></dl>

Arguments

setmapmi — setmapmi

<dl>

<dt>original_variables</dt>
<dd>Character vector of names for the original
variables that were paired (typically `pairmi_result$original.variables`).</dd>


<dt>sets</dt>
<dd>A data frame returned by [pairmi()] describing the sets. Must
contain the columns required by `setmapmi()` (e.g., identifiers for sets
and their constituent variables).</dd>


<dt>n_elements</dt>
<dd>Integer scalar giving the set size (depth) to visualize
(e.g., `2` for pairs, `3` for triplets). Must be &gt;= 1 and present in
`sets`.</dd>

</dl>

setmapmi: setmapmi

Description

Usage

Value

Arguments

Examples