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<html><head><title>R: Rough Imputation of Missing Values</title>
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<table width="100%" summary="page for na.roughfix {randomForest}"><tr><td>na.roughfix {randomForest}</td><td align="right">R Documentation</td></tr></table>
<h2>Rough Imputation of Missing Values</h2>


<h3>Description</h3>

<p>
Impute Missing Values by median/mode.
</p>


<h3>Usage</h3>

<pre>
na.roughfix(object, ...)
</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>object</code></td>
<td>
a data frame or numeric matrix.</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
further arguments special methods could require.</td></tr>
</table>

<h3>Value</h3>

<p>
A completed data matrix or data frame.  For numeric variables,
<code>NA</code>s are replaced with column medians.  For factor variables,
<code>NA</code>s are replaced with the most frequent levels (breaking ties
at random).  If <code>object</code> contains no <code>NA</code>s, it is returned
unaltered.</p>

<h3>Note</h3>

<p>
This is used as a starting point for imputing missing values by random
forest.
</p>


<h3>Author(s)</h3>

<p>
Andy Liaw
</p>


<h3>See Also</h3>

<p>
<code><a href="rfImpute.html">rfImpute</a></code>, <code><a href="randomForest.html">randomForest</a></code>.
</p>


<h3>Examples</h3>

<pre>
data(iris)
iris.na &lt;- iris
set.seed(111)
## artificially drop some data values.
for (i in 1:4) iris.na[sample(150, sample(20)), i] &lt;- NA
iris.roughfix &lt;- na.roughfix(iris.na)
iris.narf &lt;- randomForest(Species ~ ., iris.na, na.action=na.roughfix)
print(iris.narf)
</pre>



<hr><div align="center">[Package <em>randomForest</em> version 4.5-27 <a href="00Index.html">Index]</a></div>

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