plot.rcurvep_bmr

An R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) <doi:10.1289/ehp.1002476> and Sedykh A (2016) <doi:10.1007/978-1-4939-6346-1_14>). Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. 3-parameter Hill equation from 'tcpl' package (Filer D et al., <doi:10.1093/bioinformatics/btw680>) and 4-parameter Hill equation from Curve Class2 approach (Wang Y et al., <doi:10.2174/1875397301004010057>) are available. Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. <doi:10.1093/toxsci/kfy258>). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD).

Jui-Hua Hsieh

Rcurvep

Concentration-Response Data Analysis using Curvep

Alexander Sedykh

Fred Parham

Yuhong Wang

Tongan Zhao

Ruili Huang

plot.rcurvep_bmr function

<dl><dt>x</dt>
<dd>The rcurvep_bmr object from <code>estimate_dataset_bmr()</code>.</dd>
<dt>...</dt>
<dd>Allowed values: n_in_page, number of endpoints in a page.</dd></dl>

Arguments

Plot BMR diagnostic curves — plot.rcurvep_bmr

<dl>

<dt>x</dt>
<dd>The rcurvep_bmr object from <code>estimate_dataset_bmr()</code>.</dd>


<dt>...</dt>
<dd>Allowed values: n_in_page, number of endpoints in a page.</dd>

</dl>

Plot BMR diagnostic curves

plot.rcurvep_bmr: Plot BMR diagnostic curves

Description

Usage

Value

Arguments

Examples