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<table width="100%" summary="page for pboot.1sample.s"><tr><td>pboot.1sample.s</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Generate p value distributions and estimate of sample correlation matrix using bootstrap.</h2>

<h3>Description</h3>

<p>If the user chooses to keep sout as TRUE, then this function generates bootstrap distribution of p values and returns the mean of the correlation matrices of all the bootstrap samples generated.
</p>


<h3>Usage</h3>

<pre>
pboot.1sample.s(X, B = 100, test = t.test, nbx = NROW(X), ncpus = 8,
  sout = FALSE)
</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>X</code></td>
<td>
<p>data matrix</p>
</td></tr>
<tr valign="top"><td><code>B</code></td>
<td>
<p>Bootstrap size</p>
</td></tr>
<tr valign="top"><td><code>test</code></td>
<td>
<p>test to perform</p>
</td></tr>
<tr valign="top"><td><code>nbx</code></td>
<td>
<p>bootstrap sample size, by default same as the data sample size</p>
</td></tr>
<tr valign="top"><td><code>ncpus</code></td>
<td>
<p>number of cpus to use</p>
</td></tr>
<tr valign="top"><td><code>sout</code></td>
<td>
<p>if correlation matrix is needed or not</p>
</td></tr>
</table>


<h3>Value</h3>

<p>a list with a matrix containing the p value distributions, and another matrix of correlation matrix.
</p>


<h3>Examples</h3>

<pre>
## Not run: 
n = 50;m = 250;m0 = 20;
sigeff = 1;
Sigma &lt;- 0.25*diag(m)
X &lt;- datgen(n,m,m0,sigeff,Sigma = Sigma)
porder &lt;- pboot.1sample.s(X=X,B=100,sout = TRUE,ncpus = 1)
plotpboot(porder)

## End(Not run) 
</pre>


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