it_metric

Calculates a selected Information Theory-based metric based on a provided co-occurrence matrix

Builds co-occurrence matrices based on spatial raster data.
It includes creation of weighted co-occurrence matrices (wecoma) and
integrated co-occurrence matrices
(incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>).

Jakub Nowosad

comat

Creates Co-Occurrence Matrices of Spatial Data

Maximillian H.K. Hesselbarth

Marco Sciaini

Sebastian Hanss

it_metric function

<dl><dt>x</dt>
<dd>A matrix - an output of the <code>get_coma()</code> function</dd>
<dt>metric</dt>
<dd>One of the following: "ent" (Marginal entropy), "joinent" (Joint entropy), "condent" (Conditional entropy), "mutinf" (Mutual information), or "relmutinf" (Relative mutual information)</dd>
<dt>base</dt>
<dd>The unit in which entropy is measured.
The default is "log2", which compute entropy in "bits".
"log" and "log10" can be also used.</dd>
<dt>ordered</dt>
<dd>The type of pairs considered.
Either "ordered" (TRUE) or "unordered" (FALSE).
The default is TRUE.</dd></dl>

Arguments

Calculates an Information Theory-based metric — it_metric

<dl>

<dt>x</dt>
<dd>A matrix - an output of the <code>get_coma()</code> function</dd>


<dt>metric</dt>
<dd>One of the following: "ent" (Marginal entropy), "joinent" (Joint entropy), "condent" (Conditional entropy), "mutinf" (Mutual information), or "relmutinf" (Relative mutual information)</dd>


<dt>base</dt>
<dd>The unit in which entropy is measured.
The default is "log2", which compute entropy in "bits".
"log" and "log10" can be also used.</dd>


<dt>ordered</dt>
<dd>The type of pairs considered.
Either "ordered" (TRUE) or "unordered" (FALSE).
The default is TRUE.</dd>

</dl>

it_metric: Calculates an Information Theory-based metric

Description

Usage

Value

Arguments

References

Examples