predict.fitTK

predict_stan

predict_manual

Use when parameter are manually given by the user.

The MOSAICbioacc application is a turnkey package providing bioaccumulation
factors (BCF/BMF/BSAF) from a toxicokinetic (TK) model fitted to
accumulation-depuration data. It is designed to fulfil the requirements
of regulators when examining applications for market authorization of active
substances. See Ratier et al. (2021) <doi:10.1101/2021.09.08.459421>.

Virgile Baudrot

rbioacc

Inference and Prediction of ToxicoKinetic (TK) Models

Sandrine Charles

Ophélia Gestin

Miléna Kaag

Christelle Lopes

Gauthier Multari

Alain Pavé

Aude Ratier

Aurélie Siberchicot

predict.fitTK function

<dl><dt>object</dt>
<dd>An object of <code>stanfit</code></dd>
<dt>data</dt>
<dd>A data set with one column <code>time</code> and 1 to 4 exposure</dd>
<dt>mcmc_size</dt>
<dd>Size of mcmc chain if needed to be reduced</dd>
<dt>fixed_init</dt>
<dd>If <code>TRUE</code> fix the initial conditions of internal concentration.
columns with name in <code>expw</code>, <code>exps</code>, <code>expf</code> and <code>exppw</code></dd>
<dt>...</dt>
<dd>Additional arguments</dd>
<dt>time_interp</dt>
<dd>A vector with additional time point to interpolate.
Time point of the original data set are conserved.</dd>
<dt>iter</dt>
<dd>Number of time steps</dd>
<dt>param</dt>
<dd>A dataframe with name of parameters <code>kee</code>, <code>keg</code>, <code>ku1</code>,
<code>ku2</code>, ..., <code>km1</code>, <code>km2</code>, ... and <code>kem1</code>, <code>kem2</code>, ...,
<code>sigmaConc</code>, <code>sigmaCmet</code> (if metabolites) and <code>sigmaGrowth</code> (if growth).
The parameter <code>kee</code> is mandatory.</dd>
<dt>time_accumulation</dt>
<dd>the time of accumulation.</dd>
<dt>C0</dt>
<dd>Gives the initial conditions of internal concentration.</dd>
<dt>G0</dt>
<dd>initial condition of G0 (require if <code>keg</code> is provided)</dd>
<dt>gmax</dt>
<dd>gmax (require if <code>keg</code> is provided)
columns with name in <code>expw</code>, <code>exps</code>, <code>expf</code> and <code>exppw</code></dd></dl>

Arguments

Prediction function using fitTK object — predict.fitTK

<dl>

<dt>object</dt>
<dd>An object of <code>stanfit</code></dd>


<dt>data</dt>
<dd>A data set with one column <code>time</code> and 1 to 4 exposure</dd>


<dt>mcmc_size</dt>
<dd>Size of mcmc chain if needed to be reduced</dd>


<dt>fixed_init</dt>
<dd>If <code>TRUE</code> fix the initial conditions of internal concentration.
columns with name in <code>expw</code>, <code>exps</code>, <code>expf</code> and <code>exppw</code></dd>


<dt>...</dt>
<dd>Additional arguments</dd>


<dt>time_interp</dt>
<dd>A vector with additional time point to interpolate.
Time point of the original data set are conserved.</dd>


<dt>iter</dt>
<dd>Number of time steps</dd>


<dt>param</dt>
<dd>A dataframe with name of parameters <code>kee</code>, <code>keg</code>, <code>ku1</code>,
<code>ku2</code>, ..., <code>km1</code>, <code>km2</code>, ... and <code>kem1</code>, <code>kem2</code>, ...,
<code>sigmaConc</code>, <code>sigmaCmet</code> (if metabolites) and <code>sigmaGrowth</code> (if growth).
The parameter <code>kee</code> is mandatory.</dd>


<dt>time_accumulation</dt>
<dd>the time of accumulation.</dd>


<dt>C0</dt>
<dd>Gives the initial conditions of internal concentration.</dd>


<dt>G0</dt>
<dd>initial condition of G0 (require if <code>keg</code> is provided)</dd>


<dt>gmax</dt>
<dd>gmax (require if <code>keg</code> is provided)
columns with name in <code>expw</code>, <code>exps</code>, <code>expf</code> and <code>exppw</code></dd>

</dl>

Prediction function using <code>fitTK</code> object

predict.fitTK: Prediction function using `fitTK` object

Description

Usage

Value

Arguments