PMM for numeric or binary variable

internal

Multiple imputation using 'XGBoost', subsampling, and predictive mean
matching as described in Deng and Lumley (2023)
<doi:10.1080/10618600.2023.2252501>. The package supports various types of
variables, offers flexible settings, and enables saving an imputation model to impute
new data. Data processing and memory usage have been optimised to speed up
the imputation process.

Yongshi Deng

mixgb

Multiple Imputation Through 'XGBoost'

Thomas Lumley

pmm function

<dl><dt>yhatobs</dt>
<dd>The predicted values of observed entries in a variable</dd>
<dt>yhatmis</dt>
<dd>The predicted values of missing entries in a variable</dd>
<dt>yobs</dt>
<dd>The actual observed values of observed entries in a variable</dd>
<dt>k</dt>
<dd>The number of donors.</dd></dl>

Arguments

PMM for numeric or binary variable — pmm

<dl>

<dt>yhatobs</dt>
<dd>The predicted values of observed entries in a variable</dd>


<dt>yhatmis</dt>
<dd>The predicted values of missing entries in a variable</dd>


<dt>yobs</dt>
<dd>The actual observed values of observed entries in a variable</dd>


<dt>k</dt>
<dd>The number of donors.</dd>

</dl>

State of Data and AI Literacy Report 2025

pmm: PMM for numeric or binary variable

Description

Usage

Value

Arguments