conv.factor

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

conv.factor function

<dl><dt>data</dt>
<dd>Input data frame.</dd>
<dt>convNames</dt>
<dd>Names of variable to convert, the default is
<code>convNames = NULL</code>.</dd>
<dt>exceptNames</dt>
<dd>Names of variables to be excluded from conversion, the
default is <code>convNames = NULL</code>.</dd>
<dt>uniqueNum</dt>
<dd>Variables of less than or equal to a specific number of
unique values in the to be converted to factors, the default is
<code>uniqueNum = 5</code>.</dd></dl>

Arguments

Convert variables to factors — conv.factor

<dl>

<dt>data</dt>
<dd>Input data frame.</dd>


<dt>convNames</dt>
<dd>Names of variable to convert, the default is
<code>convNames = NULL</code>.</dd>


<dt>exceptNames</dt>
<dd>Names of variables to be excluded from conversion, the
default is <code>convNames = NULL</code>.</dd>


<dt>uniqueNum</dt>
<dd>Variables of less than or equal to a specific number of
unique values in the to be converted to factors, the default is
<code>uniqueNum = 5</code>.</dd>

</dl>

conv.factor: Convert variables to factors

Description

Usage

Value

Arguments

Examples