toy_data

datasets

Impute observed values below the limit of detection (LOD) via
censored likelihood multiple imputation (CLMI) in single-pollutant
models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>.
CLMI handles exposure detection limits that may change throughout the course
of exposure assessment. 'lodi' provides functions for imputing and
pooling for this method.

Alexander Rix

lodi

Limit of Detection Imputation for Single-Pollutant Models

Jonathan Boss

toy_data function

A data.frame with 100 observations on 6 variables:<dl class="dl-horizontal">
 <dt>id</dt><dd>Patient ID number.</dd>
 <dt>case_cntrl</dt><dd>Patient's case-control status. Either 1 or 0.</dd>
 <dt>poll</dt><dd>Concentration of pollutant in patient's blood sample.</dd>
 <dt>smoking</dt><dd>Smoking status. Either 1 or 0.</dd>
 <dt>gender</dt><dd>Gender. 1 for male, 0 for female.</dd>
 <dt>batch1</dt><dd>Batch status. Integer</dd>
 <dt>lod</dt><dd>batch's limit of detection for patient.</dd>
</dl>

Format

Synthetic toy data for clmi — toy_data

A data.frame with 100 observations on 6 variables:<dl class='dl-horizontal'>
 <dt>id</dt><dd>Patient ID number.</dd>
 <dt>case_cntrl</dt><dd>Patient's case-control status. Either 1 or 0.</dd>
 <dt>poll</dt><dd>Concentration of pollutant in patient's blood sample.</dd>
 <dt>smoking</dt><dd>Smoking status. Either 1 or 0.</dd>
 <dt>gender</dt><dd>Gender. 1 for male, 0 for female.</dd>
 <dt>batch1</dt><dd>Batch status. Integer</dd>
 <dt>lod</dt><dd>batch's limit of detection for patient.</dd>
</dl>

toy_data: Synthetic toy data for clmi

Description

Usage

Arguments

Format