check_data

The function `check_data()` serves the purpose of performing a preliminary check and fix some evident issues. However, the function cannot resolve all data quality-related problems.

Multiple imputation using 'XGBoost', subsampling, and predictive mean
matching as described in Deng and Lumley (2024)
<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

check_data function

<dl><dt>data</dt>
<dd>A data frame or data table.</dd>
<dt>max_levels</dt>
<dd>An integer specifying the maximum number of levels allowed for a factor variable. This is used to detect potential ID columns that are often non-informative for imputation. Default: 50% of the number of rows, rounded to the nearest integer.</dd>
<dt>verbose</dt>
<dd>Verbose setting. If <code>TRUE</code>, will print out warnings when data issues are found. Default: <code>TRUE</code>.</dd></dl>

Arguments

Sanity check for input data before imputation — check_data

<dl>

<dt>data</dt>
<dd>A data frame or data table.</dd>


<dt>max_levels</dt>
<dd>An integer specifying the maximum number of levels allowed for a factor variable. This is used to detect potential ID columns that are often non-informative for imputation. Default: 50% of the number of rows, rounded to the nearest integer.</dd>


<dt>verbose</dt>
<dd>Verbose setting. If <code>TRUE</code>, will print out warnings when data issues are found. Default: <code>TRUE</code>.</dd>

</dl>

check_data: Sanity check for input data before imputation

Description

Usage

Value

Arguments

Examples