sim_lod

<code>sim_lod()</code> simulates putting the columns of a given matrix <code>D</code> under a limit
of detection (LOD) by calculating the given quantile <code>q</code> of each column and
corrupting all values &lt; the quantile to <code>NA</code>, returning the newly corrupted
matrix, the binary corruption mask, and a vector of column LODs.

Implementation of the pattern recognition technique Principal
Component Pursuit tailored to environmental health data, as described
in Gibson et al (2022) <doi:10.1289/EHP10479>.

Lawrence G. Chillrud

pcpr

Principal Component Pursuit for Environmental Epidemiology

Jaime Benavides

Elizabeth A. Gibson

Junhui Zhang

Jingkai Yan

John N. Wright

Jeff Goldsmith

Marianthi-Anna Kioumourtzoglou

Columbia University 

sim_lod function

<dl><dt>D</dt>
<dd>The input data matrix.</dd>
<dt>q</dt>
<dd>A double in the range <code>[0, 1]</code> specifying the quantile to use
in creating the column-wise LODs. Passed as the <code>probs</code> argument to the
<code><a href="/link/quantile()?package=pcpr&version=1.0.0" data-mini-rdoc="pcpr::quantile()">quantile()</a></code> function.</dd></dl>

Arguments

Simulate limit of detection data — sim_lod

<dl>

<dt>D</dt>
<dd>The input data matrix.</dd>


<dt>q</dt>
<dd>A double in the range <code>[0, 1]</code> specifying the quantile to use
in creating the column-wise LODs. Passed as the <code>probs</code> argument to the
<code><a href='https://rdrr.io/r/stats/quantile.html'>quantile()</a></code> function.</dd>

</dl>

Simulate limit of detection data

sim_lod: Simulate limit of detection data

Description

Usage

Value

Arguments

See Also

Examples