data_clean

The function `data_clean()` 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 (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

data_clean function

<dl><dt>rawdata</dt>
<dd>A data frame.</dd>
<dt>levels.tol</dt>
<dd>Tolerant proportion of the number of levels to the number of observations in a multiclass variable. Default: 0.2</dd></dl>

Arguments

Data cleaning — data_clean

<dl>

<dt>rawdata</dt>
<dd>A data frame.</dd>


<dt>levels.tol</dt>
<dd>Tolerant proportion of the number of levels to the number of observations in a multiclass variable. Default: 0.2</dd>

</dl>

data_clean: Data cleaning

Description

Usage

Value

Arguments

Examples