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>Print contents of Ranger prediction object.
</p>

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

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p>Object of class 'ranger.prediction'.</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>further arguments passed to or from other methods.</p>
</td>
</tr>
</table>

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

print.ranger.prediction

Description

Usage

Arguments

Author(s)

ranger

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`x`	Object of class 'ranger.prediction'.
`...`	further arguments passed to or from other methods.