Package 'rbioacc' reference manual

Title:	Inference and Prediction of ToxicoKinetic (TK) Models
Description:	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>.
Authors:	Virgile Baudrot [aut, cre], Sandrine Charles [aut], Ophélia Gestin [ctb], Miléna Kaag [aut], Christelle Lopes [ctb], Gauthier Multari [ctb], Alain Pavé [ctb], Aude Ratier [aut], Aurélie Siberchicot [ctb]
Maintainer:	Virgile Baudrot <[email protected]>
License:	MIT + file LICENSE
Version:	1.1-2
Built:	2024-11-25 04:58:17 UTC
Source:	https://github.com/lbbe-software/rbioacc

The 'rbioacc' package.

Description

A DESCRIPTION OF THE PACKAGE

References

Stan Development Team (NA). RStan: the R interface to Stan. R package version NA. https://mc-stan.org

A simple implementation of to pivot_longer of tidyr

Description

A simple implementation of to pivot_longer of tidyr

Usage

.fonte(df, names_to, values_to)
.fonte(df, names_to, values_to)

Arguments

`df`	A data frame to pivot.
`names_to`	A string specifying the name of the column to create from the data stored in the column names of `df`.
`values_to`	A string specifying the name of the column to create from the data stored in cell values.

Value

The data frame with a "lengthens" shape: more rows, less columns

Return column matching "expw", "exps", "expf", "exppw" of a `data.frame`

Description

Return column matching "expw", "exps", "expf", "exppw" of a data.frame

Usage

.index_col_exposure(data_frame)
.index_col_exposure(data_frame)

Arguments

data_frame

a dataframe

Value

A vector of numeric

Return column matching "concX" of a `data.frame` where X is metabolite

Description

Return column matching "concX" of a data.frame where X is metabolite

Usage

.index_col_metabolite(data_frame)
.index_col_metabolite(data_frame)

Arguments

data_frame

a dataframe

Value

A vector of numeric

Check if two vectors `x` and y are equal after remove `Inf`

Description

Check if two vectors x and y are equal after remove Inf

Usage

.is_equal_rmInf(x, y)
.is_equal_rmInf(x, y)

Arguments

`x`	A vector
`y`	A vector

Value

A logical value

Data on Chironomus with several exposure routes.

Description

Data on Chironomus with several exposure routes.

Usage

data(Chiro_Creuzot)
data(Chiro_Creuzot)

Format

A dataframe with 24 observations on the following four variables:

time: A vector of class numeric with the time points in days.
expw: A vector of class numeric with the exposure in water.
expw: A vector of class numeric with the exposure in pore water.
replicate: A vector of class integer for replicate identification.
conc: A vector of class numeric with concentration in organism.
concm1: A vector of class numeric with metabolite concentration in organism.
concm2: A vector of class numeric with metabolite concentration in organism.

Data on Chironomus exposed to benzoapyrene

Description

Data on Chironomus exposed to benzoapyrene

Usage

data(Chironomus_benzoapyrene)
data(Chironomus_benzoapyrene)

Correlations between parameters: colored matrix

Description

Correlations between parameters: colored matrix

Usage

corrMatrix(fit)
corrMatrix(fit)

Arguments

fit

An object of class fitTK

Value

A heatmap of class ggplot.

Correlations between parameters: pairs plot

Description

Correlations between parameters: pairs plot

Usage

corrPlot(fit, plots = c("all", "deterministic", "stochastic"))
corrPlot(fit, plots = c("all", "deterministic", "stochastic"))

Arguments

`fit`	An object of class `fitTK`
`plots`	A string selecting the parameters. Defaults is `"all"` and select all parameters. Deterministc parameters can be selected by setting `"deterministic"` and stochastic parameter with `"stochastic"`

Value

A pairsplot of class ggmatrix containing planes of parameter pairs (lower triangle), marginal posterior distribution of each parameter (diagonal) and Pearson correlation coefficients (upper triangle)

PPC data.frame

Description

This is the generic ppc S3 method for plots of the predicted values along with 95\ versus the observed values for fitTK objects.

Usage

df_ppc(fit, ...)

## S3 method for class 'fitTK'
df_ppc(fit, ...)

ppc(fit, ...)

## S3 method for class 'fitTK'
ppc(fit, ...)
df_ppc(fit, ...)

## S3 method for class 'fitTK'
df_ppc(fit, ...)

ppc(fit, ...)

## S3 method for class 'fitTK'
ppc(fit, ...)

Arguments

`fit`	And object returned by fitTK
`...`	Additional arguments

Details

The black points show the observed number of survivors (pooled replicates, on $X$ -axis) against the corresponding predicted number ( $Y$ -axis). Predictions come along with 95\ intervals, which are depicted in green when they contain the observed value and in red otherwise. Samples with equal observed value are shifted on the $X$ -axis. For that reason, the bisecting line (y = x), is represented by steps when observed values are low. That way we ensure green intervals do intersect the bisecting line.

Value

A data frame with median and 95\

a plot of class ggplot

Data frame of Posterior over Prior

Description

Data frame of Posterior over Prior

Usage

df_PriorPost(fit, ...)

## S3 method for class 'fitTK'
df_PriorPost(fit, select = "all", ...)
df_PriorPost(fit, ...)

## S3 method for class 'fitTK'
df_PriorPost(fit, select = "all", ...)

Arguments

`fit`	An object of class `fitTK` returned by the function `fitTK()`.
`...`	Additional arguments
`select`	A string selecting the parameters. Defaults is `"all"` and select all parameters.Deterministc parameters can be selected by setting `"deterministic"` and stochastic parameter with `"stochastic"`

Value

An object of class data.frame

Equations of the mathematical model used for the fit

Description

Equations of the mathematical model used for the fit

Usage

equations(fit, object)
equations(fit, object)

Arguments

`fit`	An object of class `fitTK`
`object`	The data.frame used as the base as the fit object

Value

A vector of strings each containing an equation

Retrieve exposure routes names from object

Description

Retrieve exposure routes names from object

Usage

exposure_names(object)
exposure_names(object)

Arguments

object

a data frame.

Value

A vector of string

Data on Sialis lutaria exposure time series

Description

Data on Sialis lutaria exposure time series

Usage

data(Exposure_Sialis_lutaria)
data(Exposure_Sialis_lutaria)

Posterior predictive check

Description

Posterior predictive check

Bayesian inference of TK model with Stan

Bayesian inference of TK model with variable exposure profile (BETA version)

Usage

fitTK(stanTKdata, ...)

## S3 method for class 'stanTKdataCST'
fitTK(stanTKdata, ...)

## S3 method for class 'stanTKdataVAR'
fitTK(stanTKdata, ...)
fitTK(stanTKdata, ...)

## S3 method for class 'stanTKdataCST'
fitTK(stanTKdata, ...)

## S3 method for class 'stanTKdataVAR'
fitTK(stanTKdata, ...)

Arguments

`stanTKdata`	List of Data require for computing
`...`	Arguments passed to `rstan::sampling` (e.g. iter, chains).

Value

An object of class fitTK containing two object: stanTKdata the data set used for inference and stanfit returned by rstan::sampling

Data on Gammarus exposed to azoxistrobine

Description

Data on Gammarus exposed to azoxistrobine

Usage

data(Gammarus_azoxistrobine_1d_Rosch2017)
data(Gammarus_azoxistrobine_1d_Rosch2017)

Data on Sialis lutaria internal time series

Description

Data on Sialis lutaria internal time series

Usage

data(Internal_Sialis_lutaria)
data(Internal_Sialis_lutaria)

Male `Gammarus fossarum` exposed to Hg spiked water. Three exposure concentrations were tested in triplicates. The duration of the accumulation phase is 4 days for 0.0000708021 and 0.000283208 $\mu g.m L^{-1}$ exposure concentrations, and 7 days for 0.000141604 $\mu g.m L^{-1}$ exposure concentration.

Description

Male Gammarus fossarum exposed to Hg spiked water. Three exposure concentrations were tested in triplicates. The duration of the accumulation phase is 4 days for 0.0000708021 and 0.000283208 $\mu g.m L^{-1}$ exposure concentrations, and 7 days for 0.000141604 $\mu g.m L^{-1}$ exposure concentration.

Usage

data(Male_Gammarus_Merged)
data(Male_Gammarus_Merged)

Format

A dataframe with 72 observations on the following four variables:

time: A vector of class numeric with the time points in days.
expw: A vector of class numeric with Hg exposure in water in $\mu g.m L^{-1}$ .
replicate: A vector of class integer for replicate identification.
conc: A vector of class numeric with Hg concentration in organism in $\mu g.m L^{-1}$ .

References

Ciccia, T. (2019). Accumulation et devenir du mercure chez l'espèce sentinelle Gammarus fossarum : de l'expérimentation au développement d'un modèle toxicocinétique multi-compartiments. Rapport de stage de Master 2, INRAE.

Male `Gammarus pulex` exposed to seanine spiked water. A single exposure concentration was tested. The duration of the accumulation phase is 1.417 days. Three metabolites were quantified. The growth of organism was included.

Description

Male Gammarus pulex exposed to seanine spiked water. A single exposure concentration was tested. The duration of the accumulation phase is 1.417 days. Three metabolites were quantified. The growth of organism was included.

Usage

data(Male_Gammarus_seanine_growth)
data(Male_Gammarus_seanine_growth)

Format

A dataframe with 22 observations on the following four variables:

time: A vector of class numeric with the time points in days.
expw: A vector of class numeric with seanine exposure in water in $\mu g.m L^{-1}$ .
replicate: A vector of class integer for replicate identification.
conc: A vector of class numeric with concentration in organism.
concm1: A vector of class numeric with metabolite concentration in organism.
concm2: A vector of class numeric with metabolite concentration in organism.
concm3: A vector of class numeric with metabolite concentration in organism.
growth: A vector of class numeric with growth of the organism.

References

Ashauer, R. et al. (2012). Significance of xenobiotic metabolism for bioaccumulation kinetics of organic chemicals in Gammarus pulex. Environmental Science Technology, 46: 3498-3508.

Bio-accumulation data set for Gammarus fossarum exposed to Hg spiked water.

Description

Male Gammarus fossarum exposed to Hg spiked water. A single exposure concentration was tested. The duration of the accumulation phase is 4 days.

Usage

data(Male_Gammarus_Single)
data(Male_Gammarus_Single)

Format

A dataframe with 23 observations on the following four variables:

time: A vector of class numeric with the time points in days.
expw: A vector of class numeric with Hg exposure in water in $\mu g.m L^{-1}$ .
replicate: A vector of class integer for replicate identification.
conc: A vector of class numeric with Hg concentration in organism in $\mu g.m L^{-1}$ .

References

Traces of MCMC iterations

Description

Traces of MCMC iterations

Usage

mcmcTraces(fit, plots = "all")
mcmcTraces(fit, plots = "all")

Arguments

`fit`	An object of class `fitTK`
`plots`	A string selecting the parameters. Defaults is `"all"` and select all parameters. Deterministc parameters can be selected by setting `"deterministic"` and stochastic parameter with `"stochastic"`

Value

A traceplot of class ggplot.

Create a list giving data and parameters to use in the model inference.

Description

Create a list giving data and parameters to use in the model inference.

Usage

modelData(object, ...)

## S3 method for class 'data.frame'
modelData(object, time_accumulation, elimination_rate = NA, ...)
modelData(object, ...)

## S3 method for class 'data.frame'
modelData(object, time_accumulation, elimination_rate = NA, ...)

Arguments

`object`	An object of class `data.frame`
`...`	Further arguments to be passed to generic methods
`time_accumulation`	A scalar givin accumulation time
`elimination_rate`	A scalar for the elimination rate. Default is `NA`. To remove elimination rate, set `elimination_rate = 0`.

Value

A list with data and parameters require for model inference.

Create a list giving data and parameters to use in the model inference.

Description

Create a list giving data and parameters to use in the model inference.

Usage

modelData_ode(
  df_exposure,
  df_internal,
  y0 = 1,
  t0 = -0.001,
  unifMax = 10,
  time_accumulation = NULL,
  minK = -5,
  maxK = 5,
  ...
)

modelData_ode(
  df_exposure,
  df_internal,
  y0 = 1,
  t0 = -0.001,
  unifMax = 10,
  time_accumulation = NULL,
  minK = -5,
  maxK = 5,
  ...
)
modelData_ode(
  df_exposure,
  df_internal,
  y0 = 1,
  t0 = -0.001,
  unifMax = 10,
  time_accumulation = NULL,
  minK = -5,
  maxK = 5,
  ...
)

modelData_ode(
  df_exposure,
  df_internal,
  y0 = 1,
  t0 = -0.001,
  unifMax = 10,
  time_accumulation = NULL,
  minK = -5,
  maxK = 5,
  ...
)

Arguments

`df_exposure`	Dataframe of exposure with 2 column (`time` and `value`)
`df_internal`	Dataframe of internal concentration with 2 column (`time` and `value`)
`y0`	Initial concentration
`t0`	initial time point
`unifMax`	Hyperparameter value
`time_accumulation`	Time of accumulation
`minK`	Hyperparameter value
`maxK`	Hyperparameter value
`...`	Additional arguments

Value

A list with data and parameters require for model inference.

Data on Oncorhynchus exposition

Description

Data on Oncorhynchus exposition

Usage

data(Oncorhynchus_two)
data(Oncorhynchus_two)

Plot exposure profile

Description

Plot exposure profile

Usage

plot_exposure(object)
plot_exposure(object)

Arguments

object

a data frame with exposure column

Value

a plot of class ggplot

Plot Posterior over Prior

Description

Plot Posterior over Prior

Usage

plot_PriorPost(x, ...)

## S3 method for class 'fitTK'
plot_PriorPost(x, select = "all", ...)

## S3 method for class 'df_PP'
plot_PriorPost(x, select = "all", ...)
plot_PriorPost(x, ...)

## S3 method for class 'fitTK'
plot_PriorPost(x, select = "all", ...)

## S3 method for class 'df_PP'
plot_PriorPost(x, select = "all", ...)

Arguments

`x`	A data.frame of class `df_PP` returned by the function `df_PriorPost()`.
`...`	addition arguments
`select`	A string selecting the parameters. Defaults is `"all"` and select all parameters. Deterministic parameters can be selected by setting `"deterministic"` and stochastic parameter with `"stochastic"`.

Value

A plot of class ggplot.

Plot function for object of class `bioaccMetric`

Description

Plot function for object of class bioaccMetric

Usage

## S3 method for class 'bioaccMetric'
plot(x, ...)
## S3 method for class 'bioaccMetric'
plot(x, ...)

Arguments

`x`	a data frame
`...`	Additional arguments

Value

A plot of class ggplot

Plotting method for `fitTK` objects

Description

This is the generic plot S3 method for the fitTK. It plots the fit obtained for each variable in the original dataset.

Usage

## S3 method for class 'fitTK'
plot(x, time_interp = NULL, ...)
## S3 method for class 'fitTK'
plot(x, time_interp = NULL, ...)

Arguments

`x`	And object returned by fitTK
`time_interp`	A vector with additional time point to interpolate. Time point of the original data set are conserved.
`...`	Additional arguments

Value

a plot of class ggplot

Plotting method for `predictTK` objects

Description

This is the generic plot S3 method for the predictTK.

Usage

## S3 method for class 'predictTK'
plot(x, ...)

## S3 method for class 'predictTKstan'
plot(x, add_data = FALSE, ...)
## S3 method for class 'predictTK'
plot(x, ...)

## S3 method for class 'predictTKstan'
plot(x, add_data = FALSE, ...)

Arguments

`x`	An object of class `predictTK` returned by predict
`...`	Additional arguments
`add_data`	logical TRUE or FALSE to add the orignal data of the fit object `x`

Value

A plot of class ggplot

Prediction function using `fitTK` object

Description

Use when parameter are manually given by the user.

Usage

## S3 method for class 'fitTK'
predict(object, data, mcmc_size = NULL, fixed_init = TRUE, ...)

predict_stan(
  object,
  data,
  mcmc_size = NULL,
  fixed_init = TRUE,
  time_interp = NULL,
  iter = 1000,
  ...
)

predict_manual(
  param,
  data,
  time_accumulation = NULL,
  C0 = 0,
  G0 = NA,
  gmax = NA
)
## S3 method for class 'fitTK'
predict(object, data, mcmc_size = NULL, fixed_init = TRUE, ...)

predict_stan(
  object,
  data,
  mcmc_size = NULL,
  fixed_init = TRUE,
  time_interp = NULL,
  iter = 1000,
  ...
)

predict_manual(
  param,
  data,
  time_accumulation = NULL,
  C0 = 0,
  G0 = NA,
  gmax = NA
)

Arguments

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

Value

An object of class predictTK

Potential Scale Reduction Factors (PSRF) of the parameters

Description

Potential Scale Reduction Factors (PSRF) of the parameters

Usage

psrf(fit)
psrf(fit)

Arguments

fit

An object of class fitTK

Value

An object of class data.frame with two columns: PSRF and parameter

a data frame with Potential Scale Reduction Factors

Quantiles of parameters

Description

Quantiles of parameters

Usage

quantile_table(fit, probs = c(0.025, 0.5, 0.975))
quantile_table(fit, probs = c(0.025, 0.5, 0.975))

Arguments

`fit`	An object of class `fitTK`
`probs`	Scalar or Vector of quantiles. Default is 0.025, 0.5 and 0.975 giving median and 95% credible interval

Value

A data frame with quantiles

Replace element of a vector

Description

Replace element of a vector

Usage

replace_(x, from, to)
replace_(x, from, to)

Arguments

`x`	a vector
`from`	a vector of elements to replace
`to`	a vector with replacing elements

Value

a vector

Examples

 
replace_(1:10,c(2,4,5,8), c(0,0,0,0))
replace_(c(1,2,2,3,2),c(3,2), c(4,5))


replace_(1:10,c(2,4,5,8), c(0,0,0,0))
replace_(c(1,2,2,3,2),c(3,2), c(4,5))

Return the time at 95% depuration of the parent component

Description

Return the time at 95% depuration of the parent component

Usage

t95(fit)
t95(fit)

Arguments

fit

An object of class fitTK

Value

a numeric object

Widely Applicable Information Criterion (WAIC)

Description

Compute WAIC using the waic() method of the loo package.

Usage

waic(fit)
waic(fit)

Arguments

fit

An object of class fitTK

Value

A numeric containing the WAIC

`fit`	An `stanFit` object
`...`	Further arguments to be passed to generic methods
`type`	A string with the type of metric: `k` for the kinetics BioConcentration Factor, `ss` for the steady state BioConcentration Factor.
`route`	Provide exposure route: `all`

Package 'rbioacc'

Help Index

The 'rbioacc' package.

Description

References

A simple implementation of to pivot_longer of tidyr

Description

Usage

Arguments

Value

Return column matching "expw", "exps", "expf", "exppw" of a data.frame

Description

Usage

Arguments

Value

Return column matching "concX" of a data.frame where X is metabolite

Description

Usage

Arguments

Value

Check if two vectors x and y are equal after remove Inf

Description

Usage

Arguments

Value

Biaccumulation metrics

Description

Usage

Arguments

Value

Data on Chironomus with several exposure routes.

Description

Usage

Format

Data on Chironomus exposed to benzoapyrene

Description

Usage

Correlations between parameters: colored matrix

Description

Usage

Arguments

Value

Correlations between parameters: pairs plot

Description

Usage

Arguments

Value

PPC data.frame

Description

Usage

Arguments

Details

Value

Data frame of Posterior over Prior

Description

Usage

Arguments

Value

Equations of the mathematical model used for the fit

Description

Usage

Arguments

Value

Retrieve exposure routes names from object

Description

Usage

Arguments

Value

Data on Sialis lutaria exposure time series

Description

Usage

Posterior predictive check

Description

Usage

Arguments

Value

Data on Gammarus exposed to azoxistrobine

Description

Usage

Data on Sialis lutaria internal time series

Return column matching "expw", "exps", "expf", "exppw" of a `data.frame`

Return column matching "concX" of a `data.frame` where X is metabolite

Check if two vectors `x` and y are equal after remove `Inf`

Male `Gammarus pulex` exposed to seanine spiked water. A single exposure concentration was tested. The duration of the accumulation phase is 1.417 days. Three metabolites were quantified. The growth of organism was included.

Plot function for object of class `bioaccMetric`

Plotting method for `fitTK` objects

Plotting method for `predictTK` objects

Prediction function using `fitTK` object