similarity

Computes several measures of similarity
(see Choi, Cha, &amp; Tappert, 2010 for additional measures)

Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016 <http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf>), and a function to compute global network measures. Significance tests and plotting features are also implemented.

Alexander Christensen

SemNeT

Methods and Measures for Semantic Network Analysis

Alexander P. Christensen

Yoed N. Kenett

similarity function

<dl><dt>data</dt>
<dd>Matrix or data frame.
A binarized dataset of verbal fluency or linguistic data</dd>
<dt>method</dt>
<dd>Character.
Type of similarity measure to compute.
Below are the definitions for each bin:
<table class="table">
<tr><td></td><td>1</td><td>0</td><td></td><td></td></tr>
<tr><td>1</td><td>a</td><td>b</td><td>a+b</td><td>(R1)</td></tr>
<tr><td>0</td><td>c</td><td>d</td><td>c+d</td><td>(R2)</td></tr>
<tr><td></td><td>a+c</td><td>b+d</td><td>a+b+c+d</td><td>(N)</td></tr>
<tr><td></td><td>(C1)</td><td>(C2)</td><td>(N)</td><td></td></tr>
</table></dd> 
Options include:
<ul>
<li><code>"angular"</code> =
\(1 - (2 * acos(cosine similarity) / \pi)\)</li>
<li><code>"cosine"</code> =
\(a / \sqrt{(a + b)(a + c)}\)</li>
<li><code>"faith"</code> =
\(a + 0.5d / a + b + c + d\)</li>
<li><code>"jaccard"</code> =
\(a / a + b + c\)</li>
<li><code>"phi"</code> and <code>"cor"</code> =
\(ad - bc / \sqrt(R1 x R2 x C1 x C2)\)</li>
<li><code>"rr"</code> =
\(a / a + b + c + d\)</li>
</ul></dl>

Arguments

Alexander Christensen &lt;alexpaulchristensen@gmail.com&gt;

Author

Measures of Similarity — similarity

<dl>

<dt>data</dt>
<dd>Matrix or data frame.
A binarized dataset of verbal fluency or linguistic data</dd>


<dt>method</dt>
<dd>Character.
Type of similarity measure to compute.
Below are the definitions for each bin:
<table class='table'>
<tr><td></td><td>1</td><td>0</td><td></td><td></td></tr>
<tr><td>1</td><td>a</td><td>b</td><td>a+b</td><td>(R1)</td></tr>
<tr><td>0</td><td>c</td><td>d</td><td>c+d</td><td>(R2)</td></tr>
<tr><td></td><td>a+c</td><td>b+d</td><td>a+b+c+d</td><td>(N)</td></tr>
<tr><td></td><td>(C1)</td><td>(C2)</td><td>(N)</td><td></td></tr>
</table>

 
Options include:
<ul>
<li><code>"angular"</code> =
\(1 - (2 * acos(cosine similarity) / \pi)\)</li>
<li><code>"cosine"</code> =
\(a / \sqrt{(a + b)(a + c)}\)</li>
<li><code>"faith"</code> =
\(a + 0.5d / a + b + c + d\)</li>
<li><code>"jaccard"</code> =
\(a / a + b + c\)</li>
<li><code>"phi"</code> and <code>"cor"</code> =
\(ad - bc / \sqrt(R1 x R2 x C1 x C2)\)</li>
<li><code>"rr"</code> =
\(a / a + b + c + d\)</li>
</ul></dd>

</dl>

similarity: Measures of Similarity

Description

Usage

Value

Arguments

Author

References

Examples

	1	0
1	a	b	a+b	(R1)
0	c	d	c+d	(R2)
	a+c	b+d	a+b+c+d	(N)
	(C1)	(C2)	(N)