A fast implementation of Random Forests, particularly suited for high
dimensional data. Ensembles of classification, regression, survival and
probability prediction trees are supported. Data from genome-wide association
studies can be analyzed efficiently. In addition to data frames, datasets of
class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix')
can be directly analyzed.

A Fast Implementation of Random Forests

package-ubuntu-20.04-r-4.0.5#cran#ranger-0.12.1

<p>Extract variable importance of ranger object.
</p>

## S3 method for class 'ranger'
importance(x, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p>ranger object.</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>Further arguments passed to or from other methods.</p>
</td>
</tr>
</table>

<p><code><a href="ranger.html">ranger</a></code>
</p>

package-ubuntu-20.04-r-4.0.5#cran#ranger-0.12.1#docs#importance.ranger

importance.ranger

Description

Usage

Arguments

Value

Author(s)

See Also

ranger

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`x`	ranger object.
`...`	Further arguments passed to or from other methods.