generateNA

Takes a vector, matrix or <code>data.frame</code> and replaces some values by <code>NA</code>.

Alternative implementation of the beautiful 'MissForest'
algorithm used to impute mixed-type data sets by chaining random
forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012)
<doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the
lightning fast random forest package 'ranger'. Between the iterative
model fitting, we offer the option of using predictive mean matching.
This firstly avoids imputation with values not already present in the
original data (like a value 0.3334 in 0-1 coded variable). Secondly,
predictive mean matching tries to raise the variance in the resulting
conditional distributions to a realistic level. This would allow,
e.g., to do multiple imputation when repeating the call to
missRanger(). Out-of-sample application is supported as well.

Michael Mayer

missRanger

Fast Imputation of Missing Values

generateNA function

<dl><dt>x</dt>
<dd>A vector, matrix or <code>data.frame</code>.</dd>
<dt>p</dt>
<dd>Proportion of missing values to add to <code>x</code>. In case <code>x</code> is a <code>data.frame</code>,
<code>p</code> can also be a vector of probabilities per column or a named vector.</dd>
<dt>seed</dt>
<dd>An integer seed.</dd></dl>

Arguments

Adds Missing Values — generateNA

<dl>

<dt>x</dt>
<dd>A vector, matrix or <code>data.frame</code>.</dd>


<dt>p</dt>
<dd>Proportion of missing values to add to <code>x</code>. In case <code>x</code> is a <code>data.frame</code>,
<code>p</code> can also be a vector of probabilities per column or a named vector.</dd>


<dt>seed</dt>
<dd>An integer seed.</dd>

</dl>

generateNA: Adds Missing Values

Description

Usage

Value

Arguments

Examples