gen.mcar

Generate missing (completely at random) cells in a data set

An R package for multiple imputation using chained random forests.
Implemented methods can handle missing data in mixed types of variables by
using prediction-based or node-based conditional distributions constructed
using random forests. For prediction-based imputation, the method based on
the empirical distribution of out-of-bag prediction errors of random forests
and the method based on normality assumption for prediction errors of random
forests are provided for imputing continuous variables. And the method based
on predicted probabilities is provided for imputing categorical variables.
For node-based imputation, the method based on the conditional distribution
formed by the predicting nodes of random forests, and the method based on
proximity measures of random forests are provided. More details of the
statistical methods can be found in Hong et al. (2020) <arXiv:2004.14823>.

Shangzhi Hong

RfEmpImp

Multiple Imputation using Chained Random Forests

Henry S. Lynn

gen.mcar function

<dl><dt>df</dt>
<dd>Input data frame or matrix.</dd>
<dt>prop.na</dt>
<dd>Proportion of generated missing cells. The default is
<code>prop.na = 0.2</code>.</dd>
<dt>warn.empty.row</dt>
<dd>Show a warning if empty rows were present in the
output data set.</dd>
<dt>...</dt>
<dd>Other parameters (will be ignored).</dd></dl>

Arguments

Author

Generate missing (completely at random) cells in a data set — gen.mcar

<dl>

<dt>df</dt>
<dd>Input data frame or matrix.</dd>


<dt>prop.na</dt>
<dd>Proportion of generated missing cells. The default is
<code>prop.na = 0.2</code>.</dd>


<dt>warn.empty.row</dt>
<dd>Show a warning if empty rows were present in the
output data set.</dd>


<dt>...</dt>
<dd>Other parameters (will be ignored).</dd>

</dl>

gen.mcar: Generate missing (completely at random) cells in a data set

Description

Usage

Value

Arguments

Author

Examples