batch: Batch simulation of multiple exposure levels

In cvasi: Calibration, Validation, and Simulation of TKTD Models

Batch simulation of multiple exposure levels

Description

Usage

batch(
  scenario,
  exposure,
  id_col = "trial",
  format = c("long", "wide"),
  times_from = c("scenario", "exposure"),
  select = NULL
)

Arguments

scenario	a scenario object
exposure	a named list() or a data.frame with three columns
id_col	character, name of column in resulting ´data.frame' which contains a trial's name or ID
format	character, set to 'long' for long tabular format, or 'wide' for wide format
times_from	character, set to 'scenario' to use output times from scenario, or 'exposure' to take output times from each exposure series
select	optional character vector to select columns from the simulation output

Details

A convenience function to simulate a single base scenario with one or more exposure levels. The functions aims at reproducing the setup and result format of common effect studies.

Simulating a scenario is generally limited to assessing a single exposure series. However, laboratory experiments commonly examine the effects of multiple exposure levels on a biological system. A batch simulation approach involves running multiple simulations with varying exposure or treatment conditions. To illustrate: if the objective is to examine the impact of a chemical on cell growth, multiple scenarios need to be simulated to reproduce the cell growth dynamics under varying concentrations of the assessed chemical. Each simulation run will represent a specific exposure level, ranging from low to high concentrations of the chemical.

To simulate the conditions of such a laboratory experiment, the scenarios and exposure levels can either be created and simulated individually, or the batch() function can be used for ease of use.

Exposure series

The set of exposure levels can be represented by one of the following types:

• A (named) list: Each element represents an exposure level or exposure series. An exposure level can be represented by a constant numeric, a data.frame with two columns, or an ExposureSeries object. The names of the list elements specify the study ID.

• Or alternatively, a data.frame with three columns: One column for time, one for the exposure level, and one character column to specify the study IDs.

Each exposure level will be simulated using the base scenario. If the exposure levels are provided as a named list, the names will also appear in the return value of simulate(). This behavior can be used, for example, to define unique study IDs for particular exposure levels.

Exposure IDs

The list of exposure levels can be supplied as a named list. The names will be used as unique (study) IDs, so that the simulation results belonging to any exposure level can be identified in the output. If no IDs are defined by the user, generic IDs of the form 'trial{n}' will be assigned, with {n} being replaced by consecutive integers starting at one.

If the batch is passed on to simulate()', the IDs will be contained in its return value, e.g. as a dedicated column (long format) or as part of the column names (wide format).

Output format

The return value of simulate() is by default in long format, i.e. it will contain one row for each output time and exposure level. It is possible to pivot the tabular data to wide format, by setting the argument format = 'wide'.

In wide format, the output columns of each exposure level are pasted next to each other. If more than one column is pivoted per exposure level, then the exposure or study ID is added as a suffix to column names. If the output per exposure level contains only a single column (besides time and the exposure ID itself), then original column name is dropped and only exposure IDs are used. See the examples section for reference.

Select output columns

Often, only a single output column is of interest in batch simulations, such as the number of surviving individuals. To ease the interpretation and handling of the output of batch simulations, the columns contained in the output of each simulated exposure level can be filtered. One or more columns can be selected. By default, no filtering of output columns is conducted.

As an example, to create an overview of survival probabilities (S) in the GUTS-RED-IT example scenario minnow_it:

minnow_it %>%
  batch(exposure=list(0, 5, 10), select="S", format="wide") %>%
  simulate()

Value

a simulation batch object

Examples

# Simulate a batch experiment with three constant exposure levels of
# 0.0, 2.0, and 5.0 µmol/L
simulate(batch(minnow_it, list(0, 2, 5)))

# Alternatively, in tidyr style syntax
trials_list1 <- list(0, 2, 5)
minnow_it %>%
  batch(trials_list1) %>%
  simulate()

# Assign unique IDs to each exposure level
trials_list2 <- list(Control=0, TrialA=2, TrialB=5)
minnow_it %>%
  batch(trials_list2) %>%
  simulate()

# Alternatively, define multiple exposure levels in a single data.frame
trials_table <- data.frame(time=c(0, 0, 0),
                           conc=c(0, 2, 5),
                           trial=c("Control", "TrialA", "TrialB"))
minnow_it %>%
  batch(trials_table) %>%
  simulate()

# Limit simulation output to column 'S' (survival probability)
minnow_it %>%
  batch(trials_list2, select="S") %>%
  simulate()

# Return data in wide-format, unique IDs will be used as column names
minnow_it %>%
  batch(trials_list2, select="S", format="wide") %>%
  simulate()

cvasi documentation built on Sept. 11, 2025, 5:11 p.m.