convert

Converts non-factor/non-numeric variables in a data frame to factor/numeric.
Stores information to revert back.

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 jungle 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(). A
formula interface allows to control which variables should be imputed
by which.

Michael Mayer

missRanger

Fast Imputation of Missing Values

convert function

<dl><dt>X</dt>
<dd>A data frame.</dd>
<dt>check</dt>
<dd>If <code>TRUE</code>, the function checks if the converted columns can be
reverted without changes.</dd></dl>

Arguments

Conversion of non-factor/non-numeric variables. — convert

<dl>

<dt>X</dt>
<dd>A data frame.</dd>


<dt>check</dt>
<dd>If <code>TRUE</code>, the function checks if the converted columns can be
reverted without changes.</dd>

</dl>

convert: Conversion of non-factor/non-numeric variables.

Description

Usage

Value

Arguments