reg.ests

Get the estimates with corresponding confidence intervals after pooling.

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

reg.ests function

<dl><dt>obj</dt>
<dd>Pooled object from function <code>pool()</code>.</dd>
<dt>...</dt>
<dd>Other parameters to pass down.</dd></dl>

Arguments

Get regression estimates for pooled object — reg.ests

<dl>

<dt>obj</dt>
<dd>Pooled object from function <code>pool()</code>.</dd>


<dt>...</dt>
<dd>Other parameters to pass down.</dd>

</dl>

reg.ests: Get regression estimates for pooled object

Description

Usage

Value

Arguments