imputeUnivariate

Fills missing values of a vector, matrix or data frame by sampling with replacement
from the non-missing values. For data frames, this sampling is done within column.

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

imputeUnivariate function

<dl><dt>x</dt>
<dd>A vector, matrix or data frame.</dd>
<dt>v</dt>
<dd>A character vector of column names to impute (only relevant if <code>x</code>
is a data frame). The default <code>NULL</code> imputes all columns.</dd>
<dt>seed</dt>
<dd>An integer seed.</dd></dl>

Arguments

Univariate Imputation — imputeUnivariate

<dl>

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


<dt>v</dt>
<dd>A character vector of column names to impute (only relevant if <code>x</code>
is a data frame). The default <code>NULL</code> imputes all columns.</dd>


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

</dl>

imputeUnivariate: Univariate Imputation

Description

Usage

Value

Arguments

Examples