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             with parametric and nonparametric options" />




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    <h1>Partial and Semi-partial correlation</h1>
    <small class="dont-index">Source: <a href='https://github.com/jeffreyevans/spatialEco/blob/master/R/partial.cor.R'><code>R/partial.cor.R</code></a></small>
    <div class="hidden name"><code>partial.cor.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Calculates a partial or semi-partial correlation  
             with parametric and nonparametric options</p>
    </div>

    <pre class="usage"><span class='fu'>partial.cor</span><span class='op'>(</span>
  <span class='va'>x</span>,
  <span class='va'>y</span>,
  <span class='va'>z</span>,
  method <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"partial"</span>, <span class='st'>"semipartial"</span><span class='op'>)</span>,
  statistic <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"kendall"</span>, <span class='st'>"pearson"</span>, <span class='st'>"spearman"</span><span class='op'>)</span>
<span class='op'>)</span></pre>

    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
    <table class="ref-arguments">
    <colgroup><col class="name" /><col class="desc" /></colgroup>
    <tr>
      <th>x</th>
      <td><p>A vector, data.frame or matrix with 3 columns</p></td>
    </tr>
    <tr>
      <th>y</th>
      <td><p>A vector same length as x</p></td>
    </tr>
    <tr>
      <th>z</th>
      <td><p>A vector same length as x</p></td>
    </tr>
    <tr>
      <th>method</th>
      <td><p>Type of correlation: "partial" or "semipartial"</p></td>
    </tr>
    <tr>
      <th>statistic</th>
      <td><p>Correlation statistic, options are: "kendall", 
"pearson", "spearman"</p></td>
    </tr>
    </table>

    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>

    <p>data.frame containing:</p><ul>
<li><p>correlation correlation coefficient</p></li>
<li><p>p.value p-value of correlation</p></li>
<li><p>test.statistic test statistic</p></li>
<li><p>n sample size</p></li>
<li><p>Method indicating partial or semipartial correlation</p></li>
<li><p>Statistic the correlation statistic used</p></li>
</ul>

    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Partial and semipartial correlations show the association between two 
variables when one or more peripheral variables are controlled 
to hold them constant.</p>
<p>Suppose we have three variables, X, Y, and Z. Partial correlation holds 
constant one variable when computing the relations two others. Suppose we 
want to know the correlation between X and Y holding Z constant for both 
X and Y. That would be the partial correlation between X and Y controlling 
for Z. Semipartial correlation holds Z constant for either X or Y, but not 
both, so if we wanted to control X for Z, we could compute the semipartial 
correlation between X and Y holding Z constant for X.</p>
    <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2>

    <p>Jeffrey S. Evans  &lt;jeffrey_evans@tnc.org&gt;</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='va'>air.flow</span> <span class='op'>=</span> <span class='va'>stackloss</span><span class='op'>[</span>,<span class='fl'>1</span><span class='op'>]</span>
<span class='va'>water.temperature</span> <span class='op'>=</span> <span class='va'>stackloss</span><span class='op'>[</span>,<span class='fl'>2</span><span class='op'>]</span>
<span class='va'>acid</span> <span class='op'>=</span> <span class='va'>stackloss</span><span class='op'>[</span>,<span class='fl'>3</span><span class='op'>]</span>

<span class='co'># Partial using Kendall (nonparametric) correlation</span>
<span class='fu'>partial.cor</span><span class='op'>(</span><span class='va'>air.flow</span>, <span class='va'>water.temperature</span>, <span class='va'>acid</span><span class='op'>)</span>
</div><div class='output co'>#&gt;   correlation      p.value test.statistic  n  Method Statistic
#&gt; 1   0.5528912 0.0006538091        3.40825 21 partial   kendall</div><div class='input'>
<span class='va'>scholar</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></span><span class='op'>(</span>
  HSGPA<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3.0</span>, <span class='fl'>3.2</span>, <span class='fl'>2.8</span>, <span class='fl'>2.5</span>, <span class='fl'>3.2</span>, <span class='fl'>3.8</span>, <span class='fl'>3.9</span>, <span class='fl'>3.8</span>, <span class='fl'>3.5</span>, <span class='fl'>3.1</span><span class='op'>)</span>, 
   FGPA<span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>2.8</span>, <span class='fl'>3.0</span>, <span class='fl'>2.8</span>, <span class='fl'>2.2</span>, <span class='fl'>3.3</span>, <span class='fl'>3.3</span>, <span class='fl'>3.5</span>, <span class='fl'>3.7</span>, <span class='fl'>3.4</span>, <span class='fl'>2.9</span><span class='op'>)</span>,
  SATV <span class='op'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>500</span>, <span class='fl'>550</span>, <span class='fl'>450</span>, <span class='fl'>400</span>, <span class='fl'>600</span>, <span class='fl'>650</span>, <span class='fl'>700</span>, <span class='fl'>550</span>, <span class='fl'>650</span>, <span class='fl'>550</span><span class='op'>)</span><span class='op'>)</span> 

<span class='co'># Standard Pearson's correlations between HSGPA and FGPA  </span>
<span class='fu'><a href='https://rdrr.io/r/stats/cor.html'>cor</a></span><span class='op'>(</span><span class='va'>scholar</span><span class='op'>[</span>,<span class='fl'>1</span><span class='op'>]</span>, <span class='va'>scholar</span><span class='op'>[</span>,<span class='fl'>2</span><span class='op'>]</span><span class='op'>)</span>
</div><div class='output co'>#&gt; [1] 0.9226187</div><div class='input'>
<span class='co'># Partial correlation using Pearson (parametric) between HSGPA </span>
<span class='co'>#   and FGPA, controlling for SATV</span>
<span class='fu'>partial.cor</span><span class='op'>(</span><span class='va'>scholar</span>, statistic<span class='op'>=</span><span class='st'>"pearson"</span><span class='op'>)</span>
</div><div class='output co'>#&gt;   correlation    p.value test.statistic  n  Method Statistic
#&gt; 1   0.7475739 0.02057438       2.977956 10 partial   pearson</div><div class='input'>
<span class='co'># Semipartial using Pearson (parametric) correlation </span>
<span class='fu'>partial.cor</span><span class='op'>(</span>x<span class='op'>=</span><span class='va'>scholar</span><span class='op'>[</span>,<span class='fl'>2</span><span class='op'>]</span>, y<span class='op'>=</span><span class='va'>scholar</span><span class='op'>[</span>,<span class='fl'>1</span><span class='op'>]</span>, z<span class='op'>=</span><span class='va'>scholar</span><span class='op'>[</span>,<span class='fl'>3</span><span class='op'>]</span>, 
            method<span class='op'>=</span><span class='st'>"semipartial"</span>, statistic<span class='op'>=</span><span class='st'>"pearson"</span><span class='op'>)</span>
</div><div class='output co'>#&gt;   correlation   p.value test.statistic  n      Method Statistic
#&gt; 1   0.4337853 0.2434054        1.27377 10 semipartial   pearson</div><div class='input'>
</div></pre>
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