CGMExampleData

A small synthetic dataset intended for examples and tests of
<code>run_missingness_benchmark()</code>.

datasets

Evaluates predictive performance under feature-level missingness in repeated-measures continuous glucose monitoring-like data. The benchmark injects missing values at user-specified rates, imputes incomplete feature matrices using an iterative chained-equations approach inspired by multivariate imputation by chained equations (MICE; Azur et al. (2011) <doi:10.1002/mpr.329>), fits Random Forest regression models (Breiman (2001) <doi:10.1023/A:1010933404324>) and k-nearest-neighbor regression models (Zhang (2016) <doi:10.21037/atm.2016.03.37>), and reports mean absolute percentage error and R-squared across missingness rates.

Shubh Saraswat

CGMissingDataR

Missingness Benchmark for Continuous Glucose Monitoring Data

Hasin Shahed Shad 

Xiaohua Douglas Zhang

CGMExampleData function

A data frame with 250 rows and 6 variables:<dl>
<dt>LBORRES</dt>
<dd>Laboratory Observed Result for Glucose (numeric).</dd><dt>TimeSeries</dt>
<dd>Numeric feature representing time series data.</dd><dt>TimeDifferenceMinutes</dt>
<dd>Time difference in minutes between measurements (numeric).</dd><dt>USUBJID</dt>
<dd>Numeric subject identifier.</dd><dt>SiteID</dt>
<dd>Site identifier (character).</dd><dt>Visit</dt>
<dd>Visit label (character).</dd>
</dl>

Format

Example dataset for CGMissingData — CGMExampleData

A data frame with 250 rows and 6 variables:<dl>
<dt>LBORRES</dt>
<dd>Laboratory Observed Result for Glucose (numeric).</dd>

<dt>TimeSeries</dt>
<dd>Numeric feature representing time series data.</dd>

<dt>TimeDifferenceMinutes</dt>
<dd>Time difference in minutes between measurements (numeric).</dd>

<dt>USUBJID</dt>
<dd>Numeric subject identifier.</dd>

<dt>SiteID</dt>
<dd>Site identifier (character).</dd>

<dt>Visit</dt>
<dd>Visit label (character).</dd>


</dl>

CGMExampleData: Example dataset for CGMissingData

Description

Usage

Arguments

Format

Examples