Package {campsis}

Assert that one or more variable columns are present in the data.

Description

Raises an informative error listing all absent column names.

Usage

.assert_variable_present(x, variable)

Arguments

Value

invisibly NULL; called for its side-effect

Detect colour stratification columns automatically.

Description

Adds "ARM" when the column exists and contains more than one distinct value. Same logic for "SCENARIO".

Usage

.auto_colour_columns(x)

Arguments

Value

a character vector of column names to colour by, or NULL

Detect VPC stratification variable automatically.

Description

Returns a named character vector suitable for vpcPlot's strata argument (length 0 or 1), or NULL when no stratification is warranted. ARM is preferred over SCENARIO when both are multi-valued.

Usage

.auto_strata(x)

Arguments

Value

a named character vector or NULL

Does the data contain more than one replicate?

Description

Does the data contain more than one replicate?

Usage

.is_replicated(x)

Arguments

Value

TRUE if the data contains a replicate column with more than one distinct value, FALSE otherwise

Create a treatment arm.

Description

Create a treatment arm.

Usage

Arm(id = as.integer(NA), subjects = 1, label = as.character(NA))

Arguments

Value

an arm

Binomial distribution.

Description

Binomial distribution.

Usage

BinomialDistribution(trials, prob)

Arguments

Value

a binomial distribution

Create one or several bolus(es).

Description

Create one or several bolus(es).

Usage

Bolus(
  time,
  amount,
  compartment = NULL,
  f = NULL,
  lag = NULL,
  ii = NULL,
  addl = NULL,
  wrap = TRUE,
  ref = NULL,
  rep = NULL
)

Arguments

Value

a single bolus or a list of boluses

Create a bootstrap object.

Description

Create a bootstrap object.

Usage

Bootstrap(
  data,
  id = "BS_ID",
  replacement = FALSE,
  random = FALSE,
  export_id = FALSE
)

Arguments

Value

a bootstrap object

Create a bootstrap distribution. During function sampling, Campsis will generate values depending on the given data and arguments.

Description

Create a bootstrap distribution. During function sampling, Campsis will generate values depending on the given data and arguments.

Usage

BootstrapDistribution(data, replacement = FALSE, random = FALSE)

Arguments

Value

a bootstrap distribution

Create a constant distribution. Its value will be constant across all generated samples.

Description

Create a constant distribution. Its value will be constant across all generated samples.

Usage

ConstantDistribution(value)

Arguments

Value

a constant distribution (same value for all samples)

Create a non time-varying (fixed) covariate.

Description

Create a non time-varying (fixed) covariate.

Usage

Covariate(name, distribution)

Arguments

Value

a fixed covariate

Cyclic schedule constructor.

Description

Cyclic schedule constructor.

Usage

CyclicSchedule(duration, repetitions)

Arguments

Value

a cyclic schedule

Create a dataset.

Description

Create a dataset.

Usage

Dataset(subjects = NULL, label = as.character(NA), json = NULL)

Arguments

Value

a dataset

Create a dataset configuration. This configuration allows Campsis to know which are the default depot and observed compartments.

Description

Create a dataset configuration. This configuration allows Campsis to know which are the default depot and observed compartments.

Usage

DatasetConfig(
  defDepotCmt = 1,
  defObsCmt = 1,
  exportTSLD = FALSE,
  exportTDOS = FALSE,
  timeUnitDataset = "hour",
  timeUnitExport = "hour"
)

Arguments

Value

a dataset configuration

Create a dataset summary (internal method).

Description

Create a dataset summary (internal method).

Usage

DatasetSummary()

Value

a dataset summary

Create declare settings.

Description

Create declare settings.

Usage

Declare(variables = character(0))

Arguments

Value

Declare settings

Default output function (identity function).

Description

Default output function (identity function).

Usage

DefaultOutfun()

Value

an output function that returns the Campsis results as is.

Create default settings.

Description

Create default settings.

Usage

DefaultSettings(
  engine = NULL,
  seed = NULL,
  replicates = 1L,
  outvars = character(),
  outfuns = Outfuns(),
  disabled_variabilities = character(),
  dosing = FALSE
)

Arguments

Value

default settings

Discrete distribution.

Description

Discrete distribution.

Usage

DiscreteDistribution(x, prob, replace = TRUE)

Arguments

Value

a discrete distribution

Create a dose adaptation.

Description

Create a dose adaptation.

Usage

DoseAdaptation(formula, compartments = NULL)

Arguments

Value

a fixed covariate

Dosing schedule constructor.

Description

Dosing schedule constructor.

Usage

DosingSchedule()

Value

a dosing schedule (schedule repeated at dose times)

Create an ETA distribution. The resulting distribution is a normal distribution, with mean=0 and sd=sqrt(OMEGA).

Description

Create an ETA distribution. The resulting distribution is a normal distribution, with mean=0 and sd=sqrt(OMEGA).

Usage

EtaDistribution(model, omega)

Arguments

Value

an ETA distribution

Create an interruption event.

Description

Create an interruption event.

Usage

Event(name = NULL, times, fun, debug = FALSE)

Arguments

Value

an event definition

Create an event covariate. These covariates can be modified further in interruption events.

Description

Create an event covariate. These covariates can be modified further in interruption events.

Usage

EventCovariate(name, distribution)

Arguments

Value

a time-varying covariate

Create an event iteration object.

Description

Create an event iteration object.

Usage

EventIteration(index, start, end, inits = data.frame(), maxIndex)

Arguments

Value

an event iteration object

Create an event-related observations list. Please note that the provided 'times' will automatically be sorted. Duplicated times will be removed.

Description

Create an event-related observations list. Please note that the provided 'times' will automatically be sorted. Duplicated times will be removed.

Usage

EventRelatedObservations(times, compartment = NA)

Arguments

Value

observations

Create a list of interruption events.

Description

Create a list of interruption events.

Usage

Events()

Value

a events object

Create a fixed distribution. Each sample will be assigned a fixed value coming from vector 'values'.

Description

Create a fixed distribution. Each sample will be assigned a fixed value coming from vector 'values'.

Usage

FixedDistribution(values)

Arguments

Value

a fixed distribution (1 value per sample)

Create a function distribution. During distribution sampling, the provided function will be responsible for generating values for each sample. If first argument of this function is not the size (n), please tell which argument corresponds to the size 'n' (e.g. list(size="n")).

Description

Create a function distribution. During distribution sampling, the provided function will be responsible for generating values for each sample. If first argument of this function is not the size (n), please tell which argument corresponds to the size 'n' (e.g. list(size="n")).

Usage

FunctionDistribution(fun, args)

Arguments

Value

a function distribution

Create hardware settings.

Description

Create hardware settings.

Usage

Hardware(
  cpu = 1,
  replicate_parallel = FALSE,
  scenario_parallel = FALSE,
  slice_parallel = FALSE,
  slice_size = NULL,
  dataset_parallel = FALSE,
  dataset_slice_size = 500,
  auto_setup_plan = NULL
)

Arguments

Value

hardware settings

Define inter-occasion variability (IOV) into the dataset. A new variable of name 'colname' will be output into the dataset and will vary at each dose number according to the given distribution.

Description

Define inter-occasion variability (IOV) into the dataset. A new variable of name 'colname' will be output into the dataset and will vary at each dose number according to the given distribution.

Usage

IOV(colname, distribution, doseNumbers = NULL)

Arguments

Value

an IOV object

Create one or several infusion(s).

Description

Create one or several infusion(s).

Usage

Infusion(
  time,
  amount,
  compartment = NULL,
  f = NULL,
  lag = NULL,
  duration = NULL,
  rate = NULL,
  ii = NULL,
  addl = NULL,
  wrap = TRUE,
  ref = NULL,
  rep = NULL
)

Arguments

Value

a single infusion or a list of infusions.

Create a log normal distribution.

Description

Create a log normal distribution.

Usage

LogNormalDistribution(meanlog, sdlog)

Arguments

Value

a log normal distribution

Create a NCA table output function

Description

Create a NCA table output function

Usage

NCATableOutfun(table, export_type = "summary", name = "default_nca_table")

Arguments

Value

a stats_outfun object

Create NOCB settings.

Description

Create NOCB settings.

Usage

NOCB(enable = NULL, variables = character(0))

Arguments

Value

NOCB settings

Create a normal distribution.

Description

Create a normal distribution.

Usage

NormalDistribution(mean, sd)

Arguments

Value

a normal distribution

Create an observations list. Please note that the provided 'times' will automatically be sorted. Duplicated times will be removed.

Description

Create an observations list. Please note that the provided 'times' will automatically be sorted. Duplicated times will be removed.

Usage

Observations(times, compartment = NA, rep = NULL)

Arguments

Value

an observations list

Define a new occasion. Occasions are defined by mapping occasion values to dose numbers. A new column will automatically be created in the exported dataset.

Description

Define a new occasion. Occasions are defined by mapping occasion values to dose numbers. A new column will automatically be created in the exported dataset.

Usage

Occasion(colname, values, doseNumbers)

Arguments

Value

occasion object

Create a new output function

Description

Create a new output function

Usage

Outfun(
  fun = function(x, ...) {
     x
 },
  args = list(),
  packages = NULL,
  level = "replicate",
  name = "custom"
)

Arguments

Value

an output function

Create a collection of output functions.

Description

Create a collection of output functions.

Usage

Outfuns()

Value

an output_functions object

Compute the prediction interval summary over time (deprecated).

Description

Compute the prediction interval summary over time (deprecated).

Usage

PI(x, variable, strata = get_default_strata(), level = 0.9)

Arguments

Value

a summary table

Create a prediction interval output function

Description

Create a prediction interval output function

Usage

PIOutfun(
  variable,
  strata = get_default_strata(),
  level = 0.9,
  name = sprintf("pi_%s_%i%%", paste0(variable, collapse = "_"), round(level * 100))
)

Arguments

Value

a pi_outfun object

Create a parameter distribution. The resulting distribution is a log-normal distribution, with meanlog=log(THETA) and sdlog=sqrt(OMEGA).

Description

Create a parameter distribution. The resulting distribution is a log-normal distribution, with meanlog=log(THETA) and sdlog=sqrt(OMEGA).

Usage

ParameterDistribution(model, theta, omega = NULL)

Arguments

Value

a parameter distribution

Create progress settings.

Description

Create progress settings.

Usage

Progress(tick_slice = TRUE)

Arguments

Value

progress settings

'Repeat at' schedule constructor. Note that the time 0 for the base pattern will be added by default if not provided.

Description

'Repeat at' schedule constructor. Note that the time 0 for the base pattern will be added by default if not provided.

Usage

RepeatAtSchedule(times)

Arguments

Value

a 'repeat-at' schedule

Create a replace action.

Description

Create a replace action.

Usage

ReplaceAction(object)

Arguments

Value

a replace action

Create an scenario.

Description

Create an scenario.

Usage

Scenario(name = NULL, model = NULL, dataset = NULL)

Arguments

Value

a new scenario

Create a list of scenarios.

Description

Create a list of scenarios.

Usage

Scenarios(json = NULL)

Arguments

Value

a scenarios object

Create advanced simulation settings.

Description

Create advanced simulation settings.

Usage

Settings(..., json = NULL)

Arguments

Value

advanced simulation settings

Create a simulation progress object.

Description

Create a simulation progress object.

Usage

SimulationProgress(
  replicates = 1,
  scenarios = 1,
  progressor = NULL,
  hardware = NULL
)

Arguments

Value

a progress bar

Create solver settings.

Description

Create solver settings.

Usage

Solver(
  atol = 1e-08,
  rtol = 1e-08,
  hmax = NA,
  maxsteps = 70000L,
  method = "liblsoda"
)

Arguments

Value

solver settings

Create a statistics output function

Description

Create a statistics output function

Usage

StatsOutfun(
  variable,
  strata = get_default_strata(),
  stats = c("p5", "median", "p95"),
  name = sprintf("stats_%s", paste0(variable, collapse = "_"))
)

Arguments

Value

a stats_outfun object

Instantiate a new time sequence.

Description

Instantiate a new time sequence.

Usage

TimeSequence(start, end, by)

Arguments

Value

a sequence

Create a time-varying covariate. This covariate will be implemented using EVID=2 rows in the exported dataset and will not use interruption events.

Description

Create a time-varying covariate. This covariate will be implemented using EVID=2 rows in the exported dataset and will not use interruption events.

Usage

TimeVaryingCovariate(name, table)

Arguments

Value

a time-varying covariate

Instantiate a new time vector

Description

Instantiate a new time vector

Usage

TimeVector(x)

Arguments

Value

a time vector

Create an uniform distribution.

Description

Create an uniform distribution.

Usage

UniformDistribution(min, max)

Arguments

Value

an uniform distribution

Apply some action on the given object.

Description

Apply some action on the given object.

Usage

apply_action(object, action)

## S4 method for signature 'campsis_model,replace_action'
apply_action(object, action)

## S4 method for signature 'dataset,replace_action'
apply_action(object, action)

Arguments

Value

updated object

Apply compartment characteristics from model. In practice, only compartment infusion duration needs to be applied.

Description

Apply compartment characteristics from model. In practice, only compartment infusion duration needs to be applied.

Usage

apply_compartment_characteristics(table, properties)

Arguments

Value

updated dataset

Apply output function(s) on the given Campsis results.

Description

Apply output function(s) on the given Campsis results.

Usage

apply_outfun(x, outfun, level = NULL, ...)

## S4 method for signature 'outfun'
apply_outfun(x, outfun, level = NULL, ...)

## S4 method for signature 'outfuns'
apply_outfun(x, outfun, level = NULL, ...)

Arguments

Value

the updated Campsis results

Apply scenario to the given model or dataset.

Description

Apply scenario to the given model or dataset.

Usage

apply_scenario(x, scenario)

Arguments

Value

an updated model or dataset

Arm class.

Description

Arm class.

Slots

id

arm unique ID, integer

subjects

number of subjects in arm, integer

label

arm label, single character string

protocol

protocol

covariates

covariates

bootstrap

covariates to be bootstrapped

Arms class.

Description

Arms class.

Time sequence to numeric vector.

Description

Time sequence to numeric vector.

Usage

## S4 method for signature 'time_sequence'
as.numeric(x)

Arguments

Value

a numeric vector

Time vector to numeric vector.

Description

Time vector to numeric vector.

Usage

## S4 method for signature 'time_vector'
as.numeric(x)

Arguments

Value

a numeric vector

Assign dose number to each treatment entry.

Description

Assign dose number to each treatment entry.

Usage

assign_dose_number(object)

Arguments

Value

updated treatment object

Binomial distribution class.

Description

Binomial distribution class.

Bolus class.

Description

Bolus class.

Bolus wrapper class.

Description

Bolus wrapper class.

Bootstrap class.

Description

Bootstrap class.

Slots

data

data frame to be bootstrapped. Column 'BS_ID' is mandatory and corresponds to the original row ID from the bootstrap. It must be numeric and unique. Other columns are covariates to be bootstrapped (row by row).

replacement

values can be reused or not, logical

random

values are drawn randomly, logical

export_id

tell Campsis if 'BS_ID' must be exported into the dataset, logical

Bootstrap distribution class.

Description

Bootstrap distribution class.

Slots

data

values to draw, numeric vector

replacement

values can be reused or not, logical

random

values are drawn randomly, logical

Suggested Campsis handler for showing the progress bar.

Description

Suggested Campsis handler for showing the progress bar.

Usage

campsis_handler()

Value

a progressr handler list

Campsis metadata class.

Description

Campsis metadata class.

Slots

dataset

Campsis dataset object

dest

destination simulation engine

scenarios

Campsis scenarios object

outvars

character vector of output variable names

outfun

collection of output functions

replicates

number of replicates

Campsis table class (see this class as an interface)

Description

Campsis table class (see this class as an interface)

Check ii and addl arguments in addition to time.

Description

Check ii and addl arguments in addition to time.

Usage

check_ii_and_addl(time, ii, addl)

Arguments

Value

no return value

Compute incremental progress.

Description

Compute incremental progress.

Usage

compute_incremental_progress(object, tick_slice)

Arguments

Value

incremental progress, in percentage

Compute the prediction interval summary over time.

Description

Compute the prediction interval summary over time.

Usage

compute_pi(x, variable, strata = get_default_strata(), level = 0.9)

Arguments

Value

a summary table

Compute generic statistics over time.

Description

Compute generic statistics over time.

Usage

compute_stats(
  x,
  variable,
  strata = get_default_strata(),
  stats = c("p5", "median", "p95")
)

Arguments

Value

a summary table in long format

Constant distribution class.

Description

Constant distribution class.

Slots

value

covariate value, single numeric value

Convert numeric time vector based on the provided units.

Description

'r lifecycle::badge("deprecated")'

Usage

convertTime(x, from, to)

Arguments

Details

'convertTime()' is deprecated in favor of 'convert_time()'.

Value

numeric vector with the converted times

Convert numeric time vector based on the provided units.

Description

Convert numeric time vector based on the provided units.

Usage

convert_time(x, from, to)

Arguments

Value

numeric vector with the converted times

Counter-balance LOCF mode for occasions & IOV. This function will simply shift all the related occasion & IOV columns to the left (by one).

Description

Counter-balance LOCF mode for occasions & IOV. This function will simply shift all the related occasion & IOV columns to the left (by one).

Usage

counterBalanceLocfMode(table, columnNames)

Arguments

Value

2-dimensional dataset

Counter-balance NOCB mode for occasions & IOV. This function will simply shift all the related occasion & IOV columns to the right (by one).

Description

Counter-balance NOCB mode for occasions & IOV. This function will simply shift all the related occasion & IOV columns to the right (by one).

Usage

counterBalanceNocbMode(table, columnNames)

Arguments

Value

2-dimensional dataset

Covariate class.

Description

Covariate class.

Slots

name

covariate name, single character value

distribution

covariate distribution

Covariates class.

Description

Covariates class.

Cut table according to given iteration.

Description

Cut table according to given iteration.

Usage

cutTableForEvent(table, iteration, summary)

Arguments

Value

a data frame

Cyclic schedule class.

Description

Cyclic schedule class.

Slots

duration

duration of the cycle, numeric value

repetitions

number of additional repetitions to the base pattern, integer value

Dataset class.

Description

Dataset class.

Slots

arms

a list of treatment arms

config

dataset configuration for export

iiv

data frame containing the inter-individual variability (all ETAS) for the export

Dataset configuration class.

Description

Dataset configuration class.

Slots

def_depot_cmt

default depot compartment, integer

def_obs_cmt

default observation compartment, integer

export_tsld

export column TSLD, logical

export_tdos

export column TDOS, logical

time_unit_dataset

unit of time in dataset, character ('hour' by default)

time_unit_export

unit of time in export, character ('hour' by default)

Convert days to hours.

Description

Convert days to hours.

Usage

days(x)

Arguments

Value

numeric vector in hours

Declare settings class.

Description

Declare settings class.

Slots

variables

uninitialized variables to be declared, only needed with mrgsolve

Default output function class.

Description

Default output function class.

Default settings class.

Description

Default settings class.

Slots

engine

Simulation engine: 'rxode2' or 'mrgsolve'. Default is NULL (unspecified). If NULL, 'mrgsolve' is used first (if installed), followed by 'rxode2' (if installed).

seed

random seed number, integer

replicates

number of replicates, integer

outvars

output variables, character vector

outfuns

output functions, outfuns object

disabled_variabilities

variabilities to disable in the simulation, character vector

dosing

output dosing information, logical

Discrete distribution class.

Description

Discrete distribution class.

Distribution class. See this class as an interface.

Description

Distribution class. See this class as an interface.

Dose adaptation class.

Description

Dose adaptation class.

Slots

formula

formula to apply, single character string, e.g. "AMT*WT"

compartments

compartment numbers where the formula needs to be applied

Dose adaptations class.

Description

Dose adaptations class.

Filter Campsis output on dosing rows.

Description

Filter Campsis output on dosing rows.

Usage

dosing_only(x)

Arguments

Value

a data frame with the dosing rows

Dosing schedule class.

Description

Dosing schedule class.

Return the 'DROP_OTHERS' string that may be used in the 'outvars' vector for RxODE/mrgsolve to drop all others variables that are usually output in the resulting data frame.

Description

Return the 'DROP_OTHERS' string that may be used in the 'outvars' vector for RxODE/mrgsolve to drop all others variables that are usually output in the resulting data frame.

Usage

drop_others()

Value

a character value

Event class.

Description

Event class.

Slots

name

event name, character value

times

interruption times, numeric vector

fun

event function to apply at each interruption

debug

output the variables that were changed through this event

Event covariate class.

Description

Event covariate class.

Events class.

Description

Events class.

Export delegate method. This method is common to RxODE and mrgsolve.

Description

Export delegate method. This method is common to RxODE and mrgsolve.

Usage

export_delegate(object, dest, model, arm_offset = NULL, offset_within_arm = 0)

Arguments

Value

2-dimensional dataset, same for RxODE and mrgsolve

Export table delegate.

Description

Export table delegate.

Usage

export_table_delegate(model, dataset, dest, events, seed, tablefun, settings)

Arguments

Value

a data frame

Fill IOV/Occasion columns.

Description

Problem in RxODE (LOCF mode) / mrgsolve (LOCF mode), if 2 rows have the same time (often: OBS then DOSE), first row covariate value is taken! Workaround: identify these rows (group by ID and TIME) and apply a fill in the UP direction.

Usage

fillIOVOccColumns(table, columnNames, downDirectionFirst)

Arguments

Value

2-dimensional dataset

Fixed covariate class.

Description

Fixed covariate class.

Fixed distribution class.

Description

Fixed distribution class.

Slots

values

covariate values, numeric vector (1 value per sample)

Function distribution class.

Description

Function distribution class.

Slots

fun

function name, character (e.g. 'rnorm')

args

list of arguments (e.g list(mean=70, sd=10))

Generate IIV matrix for the given Campsis model.

Description

Generate IIV matrix for the given Campsis model.

Usage

generate_iiv(model, n, offset = 0)

Arguments

Value

IIV data frame with ID column

Generate IIV matrix for the given OMEGA matrix.

Description

Generate IIV matrix for the given OMEGA matrix.

Usage

generate_iiv_(omega, n)

Arguments

Value

IIV data frame

Get dataset max time.

Description

Get dataset max time.

Usage

getDatasetMaxTime(dataset)

Arguments

Value

max time of dataset, whatever its form, 2-dimensional or structured

Get list of event iterations.

Description

Get list of event iterations.

Usage

getEventIterations(events, dataset)

Arguments

Value

a list of event iterations. Note: start and end slots have the same unit as the 'exported' dataset time.

Get all time-varying variables. These variables are likely to be influenced by the NOCB mode chosen and by the 'nocbvars' vector.

Description

Get all time-varying variables. These variables are likely to be influenced by the NOCB mode chosen and by the 'nocbvars' vector.

Usage

getTimeVaryingVariables(object)

Arguments

Value

character vector with all time-varying variables of the dataset

Return the list of available time units.

Description

Return the list of available time units.

Usage

get_available_time_units()

Value

character vector

Get Campsis option logic.

Description

Get Campsis option logic.

Usage

get_campsis_option(name, default)

Arguments

Value

option value

Get the Campsis options (R options).

Description

Get the Campsis options (R options).

Usage

get_campsis_options()

Value

global options for Campsis

Get data of given column unless if does not exist (return NULL in that case).

Description

Get data of given column unless if does not exist (return NULL in that case).

Usage

get_column(.data, colname)

Arguments

Value

a vector

Get a mapping table with all possibilities of compartment names and their indexes knowing that compartment names can be provided as character or as integer.

Description

Get a mapping table with all possibilities of compartment names and their indexes knowing that compartment names can be provided as character or as integer.

Usage

get_compartment_mapping(compartments)

Arguments

Value

a tibble with two columns: INDEX and NAME

Get all covariates (fixed / time-varying / event covariates).

Description

Get all covariates (fixed / time-varying / event covariates).

Usage

get_covariates(object)

## S4 method for signature 'covariates'
get_covariates(object)

## S4 method for signature 'arm'
get_covariates(object)

## S4 method for signature 'arms'
get_covariates(object)

## S4 method for signature 'dataset'
get_covariates(object)

Arguments

Value

all covariates from object

Get all event-related covariates.

Description

Get all event-related covariates.

Usage

get_event_covariates(object)

## S4 method for signature 'covariates'
get_event_covariates(object)

## S4 method for signature 'arm'
get_event_covariates(object)

## S4 method for signature 'arms'
get_event_covariates(object)

## S4 method for signature 'dataset'
get_event_covariates(object)

Arguments

Value

all event-related covariates from object

Get all fixed covariates.

Description

Get all fixed covariates.

Usage

get_fixed_covariates(object)

## S4 method for signature 'covariates'
get_fixed_covariates(object)

## S4 method for signature 'arm'
get_fixed_covariates(object)

## S4 method for signature 'arms'
get_fixed_covariates(object)

## S4 method for signature 'dataset'
get_fixed_covariates(object)

Arguments

Value

all fixed covariates from object

Get initial conditions at simulation start-up.

Description

Get initial conditions at simulation start-up.

Usage

get_initial_conditions(subdataset, iteration, cmtNames)

Arguments

Value

named numeric vector with the new initial conditions

Get all IOV objects.

Description

Get all IOV objects.

Usage

get_iovs(object)

## S4 method for signature 'arm'
get_iovs(object)

## S4 method for signature 'arms'
get_iovs(object)

## S4 method for signature 'dataset'
get_iovs(object)

Arguments

Value

all IOV's from object

Get all occasions.

Description

Get all occasions.

Usage

get_occasions(object)

## S4 method for signature 'arm'
get_occasions(object)

## S4 method for signature 'arms'
get_occasions(object)

## S4 method for signature 'dataset'
get_occasions(object)

Arguments

Value

all occasions from object

Get random seed value.

Description

Get random seed value.

Usage

get_random_seed_value()

Value

random seed value generated based on time

Get seed value.

Description

Get seed value.

Usage

get_seed(seed = NULL)

Arguments

Value

a seed value, integer

Get seed for dataset export.

Description

Get seed for dataset export.

Usage

get_seed_for_dataset_export(seed, progress)

Arguments

Value

the seed value used to export the dataset

Get seed for iteration.

Description

Get seed for iteration.

Usage

get_seed_for_iteration(seed, progress)

Arguments

Value

the seed value to be used for the given replicate number and iteration

Get seed for parameter uncertainty sampling.

Description

Get seed for parameter uncertainty sampling.

Usage

get_seed_for_parameters_sampling(seed)

Arguments

Value

the seed value used to sample parameter uncertainty

Get simulation engine type.

Description

Get simulation engine type.

Usage

get_simulation_engine_type(dest)

Arguments

Value

simulation engine type

Get splitting configuration for parallel export.

Description

Get splitting configuration for parallel export.

Usage

get_splitting_configuration(dataset, hardware)

Arguments

Value

splitting configuration list (if 'dataset_parallel' is enabled) or NA (if 'dataset_parallel' disabled or if the length of the dataset is less than the dataset export slice size)

Get all time-varying covariates.

Description

Get all time-varying covariates.

Usage

get_time_varying_covariates(object)

## S4 method for signature 'covariates'
get_time_varying_covariates(object)

## S4 method for signature 'arm'
get_time_varying_covariates(object)

## S4 method for signature 'arms'
get_time_varying_covariates(object)

## S4 method for signature 'dataset'
get_time_varying_covariates(object)

Arguments

Value

all time-varying covariates from object

Get all distinct times for the specified object.

Description

Get all distinct times for the specified object.

Usage

get_times(object, ...)

## S4 method for signature 'treatment'
get_times(object, unwrap = TRUE)

## S4 method for signature 'observations'
get_times(object, doseTimes = NULL)

## S4 method for signature 'observations_set'
get_times(object, doseTimes = NULL)

## S4 method for signature 'arm'
get_times(object)

## S4 method for signature 'arms'
get_times(object)

## S4 method for signature 'events'
get_times(object)

## S4 method for signature 'dataset'
get_times(object)

Arguments

Value

numeric vector with all unique times, sorted

Hardware settings class.

Description

Hardware settings class.

Slots

cpu

number of CPU cores to use, default is 1

replicate_parallel

enable parallel computing for replicates, default is FALSE

scenario_parallel

enable parallel computing for scenarios, default is FALSE

slice_parallel

enable parallel computing for slices, default is FALSE

slice_size

number of subjects per simulated slice, default is NULL (auto-configured by Campsis depending on the specified engine)

dataset_parallel

enable parallelisation when exporting dataset into a table, default is FALSE

dataset_slice_size

dataset slice size when exporting subjects to a table, default is 500. Only applicable if 'dataset_parallel' is enabled.

auto_setup_plan

auto-setup plan with the library future, default is FALSE

Convert hours to hours (do nothing).

Description

Convert hours to hours (do nothing).

Usage

hours(x)

Arguments

Value

numeric vector in hours

Import the whole campsismod package into NAMESPACE when parsed by 'roxygen'.

Description

Import the whole campsismod package into NAMESPACE when parsed by 'roxygen'.

Usage

import_campsismod_to_namespace()

Value

always TRUE

Infusion class.

Description

Infusion class.

Slots

duration

infusion duration, distribution list

rate

infusion rate, distribution list

Infusion wrapper class.

Description

Infusion wrapper class.

Internal settings class (transient object from the simulation settings).

Description

Internal settings class (transient object from the simulation settings).

Slots

dataset_summary

dataset summary

progress

simulation progress

iterations

list of event iterations

Is the given bootstrap empty.

Description

Is the given bootstrap empty.

Usage

is_empty_bootstrap(object)

Arguments

Value

logical value TRUE/FALSE

JSON to Campsis dataset.

Description

JSON to Campsis dataset.

Usage

json_to_campsis_dataset(object, json)

Arguments

Value

Campsis dataset

JSON to Campsis dataset.

Description

JSON to Campsis dataset.

Usage

json_to_campsis_settings(object, json)

Arguments

Value

Campsis dataset

Left-join IIV matrix.

Description

Left-join IIV matrix.

Usage

left_join_iiv(table, iiv)

Arguments

Value

updated table with IIV matrix

Return the number of subjects contained in this arm.

Description

Return the number of subjects contained in this arm.

Usage

## S4 method for signature 'arm'
length(x)

Arguments

Value

a number

Return the number of repetition cycles.

Description

Return the number of repetition cycles.

Usage

## S4 method for signature 'cyclic_schedule'
length(x)

Arguments

Value

a number

Return the number of subjects contained in this dataset.

Description

Return the number of subjects contained in this dataset.

Usage

## S4 method for signature 'dataset'
length(x)

Arguments

Value

a number

Return the number of repetition cycles.

Description

Return the number of repetition cycles.

Usage

## S4 method for signature 'repeat_at_schedule'
length(x)

Arguments

Value

a number

Log normal distribution class.

Description

Log normal distribution class.

Make the VPC summary. Input data frame must contain the following columns: - replicate: replicate number - low: low percentile value in replicate (and in scenario if present) - med: median value in replicate (and in scenario if present) - up: up percentile value in replicate (and in scenario if present) - any scenario column

Description

Make the VPC summary. Input data frame must contain the following columns: - replicate: replicate number - low: low percentile value in replicate (and in scenario if present) - med: median value in replicate (and in scenario if present) - up: up percentile value in replicate (and in scenario if present) - any scenario column

Usage

make_vpc_summary(x, strata = NULL, level = 0.9)

Arguments

Value

VPC summary with columns TIME, stratification variables and all combinations of low, med, up (i.e. low_low, low_med, low_up, etc.)

Merge time-varying covariates into a single data frame. This last data frame will be merged afterwards with all treatment and observation rows.

Description

Merge time-varying covariates into a single data frame. This last data frame will be merged afterwards with all treatment and observation rows.

Usage

mergeTimeVaryingCovariates(covariates, ids_within_arm, arm_offset)

Arguments

Value

a data.frame

Convert minutes to hours.

Description

Convert minutes to hours.

Usage

minutes(x)

Arguments

Value

numeric vector in hours

Convert pharma months (1 month = 4 weeks) to hours.

Description

Convert pharma months (1 month = 4 weeks) to hours.

Usage

months(x)

Arguments

Value

numeric vector in hours

mrgsolve engine class.

Description

mrgsolve engine class.

NCA table output function class.

Description

NCA table output function class.

Slots

table

Campsisnca table object

export_type

type of export, 'summary', 'summary_wide', 'summary_pretty', 'individual' or 'individual_wide'

NHANES database (demographics and body measure data combined, from 2017-2018).

Description

NHANES database (demographics and body measure data combined, from 2017-2018).

Usage

nhanes

Format

data frame

BS_ID

Original identifier

SEX

Sex: 1 for males, 2 for females

AGE

Age in years

BW

Body weight in kg

BMI

Body mass index

HT

Height in cm

Source

https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/DEMO_J.XPT

https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/BMX_J.XPT

NOCB settings class.

Description

NOCB settings class.

Slots

enable

enable/disable next-observation carried backward mode (NOCB), default value is TRUE for mrgsolve, FALSE for RxODE

variables

variable names subject to NOCB behavior (see vignette for more info)

Normal distribution class.

Description

Normal distribution class.

Filter Campsis output on observation rows.

Description

Filter Campsis output on observation rows.

Usage

obs_only(x)

Arguments

Value

a data frame with the observation rows

Observations class.

Description

Observations class.

Slots

times

any object that implements

compartment

compartment index (integer) or name (character)

dv

observed values, numeric vector (FOR EXTERNAL USE)

rep

repetition schedule

Observations set class.

Description

Observations set class.

Occasion class.

Description

Occasion class.

Slots

colname

single character value representing the column name related to this occasion

values

occasion values, integer vector, same length as dose_numbers

dose_numbers

associated dose numbers, integer vector, same length as values

Occasions class.

Description

Occasions class.

Check if the current session is on CI (e.g. GitHub actions).

Description

Check if the current session is on CI (e.g. GitHub actions).

Usage

on_ci()

Value

logical value TRUE/FALSE

Check if the current session is on CRAN. The objective is to potentially suppress long tasks to be run on CRAN (long tests or vignettes).

Description

Check if the current session is on CRAN. The objective is to potentially suppress long tasks to be run on CRAN (long tests or vignettes).

Usage

on_cran()

Value

logical value TRUE/FALSE

Open JSON file.

Description

Open JSON file.

Usage

open_json(json, schema = NULL)

Arguments

Value

parsed JSON object

Output function class.

Description

Output function class.

Slots

fun

function or purrr-style lambda formula, first argument 'x' must be the results

name

name of the output function

args

extra arguments, named list

packages

packages that must be loaded to execute the given function, character vector

level

either 'scenario' or 'replicate'. Default is 'scenario'.

cls

resulting S3 class(es) of the Campsis output

outfun_as_arg

logical, whether to pass the outfun object as an argument to the function

Output functions class (i.e. a collection of output functions).

Description

Output functions class (i.e. a collection of output functions).

Prediction Interval (PI) Campsis table class

Description

Prediction Interval (PI) Campsis table class

Prediction interval output function class.

Description

Prediction interval output function class.

Slots

variable

variable(s) used to compute the prediction interval, character vector

strata

named vector with the strata to use

pi_level

PI level, default is 0.9 (90% PI)

Preprocess 'nocbvars' argument.

Description

Preprocess 'nocbvars' argument.

Usage

preprocessNocbvars(nocbvars)

Arguments

Preprocess TSLD and TDOS columns according to given dataset configuration.

Description

Preprocess TSLD and TDOS columns according to given dataset configuration.

Usage

preprocessTSLDAndTDOSColumn(table, config)

Arguments

Value

updated table

Preprocess ARM column. Add ARM equation in model automatically.

Description

Preprocess ARM column. Add ARM equation in model automatically.

Usage

preprocess_arm_column(dataset, model)

Arguments

Value

updated model

Pre-process destination engine. Throw an error message if the destination engine is not installed.

Description

Pre-process destination engine. Throw an error message if the destination engine is not installed.

Usage

preprocess_dest(dest)

Arguments

Value

'rxode2' or 'mrgsolve'

Preprocess 'dosing' argument.

Description

Preprocess 'dosing' argument.

Usage

preprocess_dosing(dosing)

Arguments

Value

user value, if not specified, return FALSE (observations only)

Pre-process events.

Description

Pre-process events.

Usage

preprocess_events(events)

Arguments

Preprocess subjects ID's.

Description

Preprocess subjects ID's.

Usage

preprocess_ids(dataset)

Arguments

Value

list of consecutive ID's

Pre-process outfun argument.

Description

Pre-process outfun argument.

Usage

preprocess_outfun(outfun)

Arguments

Value

an 'outfuns' object in any case

Preprocess 'outvars' argument. 'Outvars' is a character vector which tells Campsis the mandatory columns to keep in the output dataframe.

Description

Preprocess 'outvars' argument. 'Outvars' is a character vector which tells Campsis the mandatory columns to keep in the output dataframe.

Usage

preprocess_outvars(outvars)

Arguments

Value

outvars

Preprocess 'replicates' argument.

Description

Preprocess 'replicates' argument.

Usage

preprocess_replicates(replicates, model)

Arguments

Value

number of replicates to simulate

Pre-process scenarios.

Description

Pre-process scenarios.

Usage

preprocess_scenarios(scenarios)

Arguments

Preprocess the simulation settings.

Description

Preprocess the simulation settings.

Usage

preprocess_settings(settings, dest)

Arguments

Value

updated simulation settings

Preprocess 'slices' argument.

Description

Preprocess 'slices' argument.

Usage

preprocess_slices(slices, maxID)

Arguments

Value

slices if not NULL, otherwise total number of subjects

Pre-process tablefun argument.

Description

Pre-process tablefun argument.

Usage

preprocess_tablefun(fun)

Arguments

Value

a function in any case

Process time-related columns according to given dataset configuration.

Description

Process time-related columns according to given dataset configuration.

Usage

processAllTimeColumns(table, config)

Arguments

Value

updated table

Process arm labels. Arm identifiers in ARM column are replaced by arm labels as soon as one arm label is provided.

Description

Process arm labels. Arm identifiers in ARM column are replaced by arm labels as soon as one arm label is provided.

Usage

process_arm_labels(campsis, arms)

Arguments

Value

updated Campsis output with arm labels instead of arm identifiers

Process 'DROP_OTHERS'.

Description

Process 'DROP_OTHERS'.

Usage

process_drop_others(x, outvars = character(0), drop_others)

Arguments

Value

processed data frame

Preprocess arguments of the simulate method.

Description

Preprocess arguments of the simulate method.

Usage

process_simulate_arguments(model, dataset, dest, outvars, dosing, settings)

Arguments

Value

a simulation configuration

Progress settings class.

Description

Progress settings class.

Slots

tick_slice

tick() is called after each simulated slice, default is TRUE. In some cases, when the number of subjects per slice is low, it may be useful disable this flag, to improve performance issues.

Protocol class.

Description

Protocol class.

Remove initial conditions.

Description

Remove initial conditions.

Usage

remove_initial_conditions(model)

Arguments

Value

same model without initial conditions

Reorder output columns.

Description

Reorder output columns.

Usage

reorder_columns(results, dosing)

Arguments

Value

reordered dataframe

'Repeat at' schedule class.

Description

'Repeat at' schedule class.

Slots

times

times at which the event is repeated, numeric vector

Repeat schedule.

Description

Repeat schedule.

Usage

repeat_schedule(x, schedule)

## S4 method for signature 'numeric,cyclic_schedule'
repeat_schedule(x, schedule)

## S4 method for signature 'numeric,repeat_at_schedule'
repeat_schedule(x, schedule)

## S4 method for signature 'numeric,undefined_schedule'
repeat_schedule(x, schedule)

Arguments

Value

resulting times vector

Repeated schedule class. See this class as an interface.

Description

Repeated schedule class. See this class as an interface.

Replace action class.

Description

Replace action class.

Retrieve the parameter value (standardized) for the specified parameter name.

Description

Retrieve the parameter value (standardized) for the specified parameter name.

Usage

retrieve_parameter_value(model, paramName, default = NULL, mandatory = FALSE)

Arguments

Value

the standardized parameter value or the given default value if not found

rxode2 engine class.

Description

rxode2 engine class.

Sample generic object.

Description

Sample generic object.

Usage

sample(object, n, ...)

## S4 method for signature 'constant_distribution,integer'
sample(object, n)

## S4 method for signature 'fixed_distribution,integer'
sample(object, n)

## S4 method for signature 'function_distribution,integer'
sample(object, n)

## S4 method for signature 'bootstrap_distribution,integer'
sample(object, n)

## S4 method for signature 'bolus,integer'
sample(object, n, ...)

## S4 method for signature 'infusion,integer'
sample(object, n, ...)

## S4 method for signature 'observations,integer'
sample(object, n, ...)

## S4 method for signature 'covariate,integer'
sample(object, n)

## S4 method for signature 'bootstrap,integer'
sample(object, n)

Arguments

Value

sampling result

Sample time-varying covariates.

Description

Sample time-varying covariates.

Usage

sampleTimeVaryingCovariates(object, armID, needsDV)

Arguments

Value

a data.frame

Sample covariates list.

Description

Sample covariates list.

Usage

sample_covariates_list(covariates, ids_within_arm, subset)

Arguments

Value

a dataframe of n rows, 1 column per covariate

Sample a distribution and return a tibble.

Description

Sample a distribution and return a tibble.

Usage

sample_distribution_as_tibble(distribution, n, colname)

Arguments

Value

a tibble of n rows and 1 column

Scatter plot (or X vs Y plot).

Description

Scatter plot (or X vs Y plot).

Usage

scatterPlot(x, variable, colour = NULL, time = NULL)

Arguments

Value

a ggplot object

Scatter plot (S3 generic).

Description

Scatter plot (S3 generic).

Usage

scatter_plot(x, ...)

Arguments

Value

a ggplot object

Scatter plot for standard Campsis simulation output.

Description

Plots one or two variables against each other at a given time point, from a std_campsis_tbl (the default output of simulate() when no custom outfun is used).

Usage

## S3 method for class 'std_campsis_tbl'
scatter_plot(x, variable = "CONC", colour = "auto", time = NULL, ...)

Arguments

Details

When a single variable is supplied it is plotted on the x-axis with y fixed to 0, producing a 1-D strip chart. When two variables are supplied the first is mapped to x and the second to y.

Colour stratification is resolved as follows:

• "auto" (default): ARM is added when more than one distinct arm is detected in the data; SCENARIO is added when more than one distinct scenario is detected.

• NULL: no colour mapping.

• character vector: explicit column name(s) to colour by (forwarded directly to scatterPlot).

Value

a ggplot object

See Also

scatterPlot

Scenario class.

Description

Scenario class.

Slots

name

scenario name, single character string

model

either a Campsis model, a function or lambda-style formula

dataset

either a Campsis dataset, a function or lambda-style formula

actions

list of actions to apply

Scenario action class.

Description

Scenario action class.

Scenario actions class.

Description

Scenario actions class.

Scenarios class.

Description

Scenarios class.

Convert seconds to hours.

Description

Convert seconds to hours.

Usage

seconds(x)

Arguments

Value

numeric vector in hours

Set the label.

Description

'r lifecycle::badge("deprecated")'

Usage

setLabel(object, x)

Arguments

Details

'setLabel()' is deprecated in favor of 'set_label()'.

Value

the updated object

Set the number of subjects.

Description

'r lifecycle::badge("deprecated")'

Usage

setSubjects(object, x)

Arguments

Details

'setSubjects()' is deprecated in favor of 'set_subjects()'.

Value

the updated object

Set the label.

Description

Set the label.

Usage

set_label(object, x)

## S4 method for signature 'arm,character'
set_label(object, x)

## S4 method for signature 'dataset,character'
set_label(object, x)

Arguments

Value

the updated object

Set the seed. The goal of this method is to centralize all calls to the R method 'set.seed'.

Description

Set the seed. The goal of this method is to centralize all calls to the R method 'set.seed'.

Usage

set_seed(seed)

Arguments

Set the number of subjects.

Description

Set the number of subjects.

Usage

set_subjects(object, x)

## S4 method for signature 'arm,integer'
set_subjects(object, x)

## S4 method for signature 'dataset,integer'
set_subjects(object, x)

Arguments

Value

the updated object

Setup default plan for the given simulation or hardware settings. This plan will prioritise the distribution of workers in the following order: 1) Replicates (if 'replicate_parallel' is enabled) 2) Scenarios (if 'scenario_parallel' is enabled) 3) Dataset export / slices (if 'dataset_export' or 'slice_parallel' is enabled)

Description

'r lifecycle::badge("deprecated")'

Usage

setupPlanDefault(object)

Arguments

Details

'setupPlanDefault()' is deprecated in favor of 'setup_plan_default()'.

Value

nothing

Setup plan as sequential (i.e. no parallelisation).

Description

'r lifecycle::badge("deprecated")'

Usage

setupPlanSequential()

Details

'setupPlanSequential()' is deprecated in favor of 'setup_plan_sequential()'.

Value

nothing

Setup default plan for the given simulation or hardware settings. This plan will prioritise the distribution of workers in the following order: 1) Replicates (if 'replicate_parallel' is enabled) 2) Scenarios (if 'scenario_parallel' is enabled) 3) Dataset export / slices (if 'dataset_export' or 'slice_parallel' is enabled)

Description

Setup default plan for the given simulation or hardware settings. This plan will prioritise the distribution of workers in the following order: 1) Replicates (if 'replicate_parallel' is enabled) 2) Scenarios (if 'scenario_parallel' is enabled) 3) Dataset export / slices (if 'dataset_export' or 'slice_parallel' is enabled)

Usage

setup_plan_default(object)

Arguments

Value

nothing

Setup plan as sequential (i.e. no parallelisation).

Description

Setup plan as sequential (i.e. no parallelisation).

Usage

setup_plan_sequential()

Value

nothing

Shaded plot (or prediction interval plot).

Description

Shaded plot (or prediction interval plot).

Usage

shadedPlot(
  x,
  variable,
  colour = NULL,
  strat_extra = NULL,
  level = 0.9,
  alpha = 0.25
)

Arguments

Value

a ggplot object

Shaded plot / prediction interval plot (S3 generic).

Description

Shaded plot / prediction interval plot (S3 generic).

Usage

shaded_plot(x, ...)

Arguments

Value

a ggplot object

Shaded plot for standard Campsis simulation output.

Description

Plots a median line with a prediction interval ribbon from a std_campsis_tbl (the default output of simulate() when no custom outfun is used).

Usage

## S3 method for class 'std_campsis_tbl'
shaded_plot(
  x,
  variable = "CONC",
  colour = "auto",
  strat_extra = NULL,
  level = 0.9,
  alpha = 0.25,
  ...
)

Arguments

Details

Colour stratification is resolved as follows:

• "auto" (default): ARM is added when more than one distinct arm is detected in the data; SCENARIO is added when more than one distinct scenario is detected.

• NULL: no colour mapping.

• character vector: explicit column name(s) to colour/fill by (forwarded directly to shadedPlot).

Value

a ggplot object

See Also

shadedPlot

Simulate function.

Description

Simulate function.

Usage

simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = NULL,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'replicated_campsis_model,
##   dataset,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   outfuns,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = NULL,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   dataset,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   outfuns,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = NULL,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   tbl_df,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   outfuns,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = NULL,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   data.frame,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   outfuns,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = NULL,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   tbl_df,
##   rxode_engine,
##   events,
##   scenarios,
##   function,
##   character,
##   outfuns,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = NULL,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   tbl_df,
##   mrgsolve_engine,
##   events,
##   scenarios,
##   function,
##   character,
##   outfuns,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = NULL,
  dosing = FALSE,
  settings = NULL
)

Arguments

Value

dataframe with all results

Simulation delegate (several replicates).

Description

Simulation delegate (several replicates).

Usage

simulate_delegate(
  model,
  dataset,
  dest,
  events,
  scenarios,
  tablefun,
  outvars,
  outfun,
  seed,
  replicates,
  dosing,
  settings
)

Arguments

Value

a data frame with the results

Simulation delegate core (single replicate).

Description

Simulation delegate core (single replicate).

Usage

simulate_delegate_core(
  model,
  dataset,
  dest,
  events,
  tablefun,
  outvars,
  outfun,
  seed,
  replicates,
  dosing,
  settings
)

Arguments

Value

a data frame with the results

Simulation scenarios.

Description

Simulation scenarios.

Usage

simulate_scenarios(
  scenarios,
  model,
  dataset,
  dest,
  events,
  tablefun,
  outvars,
  outfun,
  seed,
  replicates,
  dosing,
  settings
)

Arguments

Value

a data frame with the results

Simulation engine class.

Description

Simulation engine class.

Simulation progress class.

Description

Simulation progress class.

Slots

replicates

total number of replicates to simulate

scenarios

total number of scenarios to simulate

iterations

total number of iterations to simulate

slices

total number of slices to simulate

replicate

current replicate number being simulated

scenario

current scenario number being simulated

iteration

current iteration number being simulated

slice

current slice number being simulated

progressor

progressr progressor

hardware

hardware settings

Simulation settings class.

Description

Simulation settings class.

Slots

default

default settings of the simulate method

hardware

hardware settings object

solver

solver settings object

nocb

NOCB settings object

declare

declare settings (mrgsolve only)

progress

progress settings

replication

replication settings

internal

internal settings

Solver settings class. See ?mrgsolve::update. See ?rxode2::rxSolve.

Description

Solver settings class. See ?mrgsolve::update. See ?rxode2::rxSolve.

Slots

atol

absolute solver tolerance, default is 1e-08

rtol

relative solver tolerance, default is 1e-08

hmax

limit how big a solver step can be, default is NA

maxsteps

max steps between 2 integration times (e.g. when observations records are far apart), default is 70000

method

solver method, for rxode2 only: 'liblsoda' (default), 'lsoda', 'dop853', 'indLin'. Mrgsolve's method is always 'lsoda'.

Spaghetti plot.

Description

Spaghetti plot.

Usage

spaghettiPlot(x, variable, colour = NULL)

Arguments

Value

plot

Spaghetti plot (S3 generic).

Description

Spaghetti plot (S3 generic).

Usage

spaghetti_plot(x, ...)

Arguments

Value

a ggplot object

Spaghetti plot for standard Campsis simulation output.

Description

Plots individual-level time profiles from a std_campsis_tbl (the default output of simulate() when no custom outfun is used).

Usage

## S3 method for class 'std_campsis_tbl'
spaghetti_plot(x, variable = "CONC", colour = "auto", ...)

Arguments

Details

Colour stratification is resolved as follows:

• "auto" (default): ARM is added when more than one distinct arm is detected in the data; SCENARIO is added when more than one distinct scenario is detected.

• NULL: no colour mapping.

• character vector: explicit column name(s) to colour by (forwarded directly to spaghettiPlot).

Value

a ggplot object

See Also

spaghettiPlot

Split dataset according to config.

Description

Split dataset according to config.

Usage

splitDataset(dataset, config)

Arguments

Value

a subset of the given dataset

Standardise time to hours.

Description

Standardise time to hours.

Usage

standardise_time(x, unit)

Arguments

Value

numeric vector with the times converted to hours

Statistics Campsis table class

Description

Statistics Campsis table class

Statistics output function class.

Description

Statistics output function class.

Slots

variable

variable(s) used to compute the statistics, character vector

strata

named vector with the strata to use

stats

character vector of statistics to compute

Standard Campsis table class

Description

Standard Campsis table class

Time sequence class.

Description

Time sequence class.

Time-varying covariate class.

Description

Time-varying covariate class.

Time vector class.

Description

Time vector class.

Convert dataset to dataset summary (internal method).

Description

Convert dataset to dataset summary (internal method).

Usage

toDatasetSummary(dataset)

Value

a dataset summary

Convert user-given distribution to an explicit Campsis distribution. Passed distribution can be: - a NULL value. In that case, it will be converted into an 'UndefinedDistribution'. - a single numeric value. In that case, it will be converted into a 'ConstantDistribution'. - a numeric vector. In that case, it will be converted into a 'FixedDistribution'. - all available types of distribution. In this case, no conversion is applied.

Description

Convert user-given distribution to an explicit Campsis distribution. Passed distribution can be: - a NULL value. In that case, it will be converted into an 'UndefinedDistribution'. - a single numeric value. In that case, it will be converted into a 'ConstantDistribution'. - a numeric vector. In that case, it will be converted into a 'FixedDistribution'. - all available types of distribution. In this case, no conversion is applied.

Usage

to_explicit_distribution(distribution)

Arguments

Value

a distribution object

Treatment class.

Description

Treatment class.

Treatment IOV class.

Description

Treatment IOV class.

Slots

colname

name of the column that will be output in dataset

distribution

distribution

dose_numbers

associated dose numbers, integer vector, same length as values

Treatment IOV's class.

Description

Treatment IOV's class.

Undefined distribution class. This type of object is automatically created in method to_explicit_distribution() when the user does not provide a concrete distribution. This is because S4 objects do not accept NULL values.

Description

Undefined distribution class. This type of object is automatically created in method to_explicit_distribution() when the user does not provide a concrete distribution. This is because S4 objects do not accept NULL values.

Undefined schedule class.

Description

Undefined schedule class.

Uniform distribution class.

Description

Uniform distribution class.

Unite the given column names.

Description

Unite the given column names.

Usage

unite_columns(x, columns, colname, factor = TRUE)

Arguments

Value

a data frame

Unwrap treatment.

Description

Unwrap treatment.

Usage

unwrap_treatment(object)

## S4 method for signature 'bolus'
unwrap_treatment(object)

## S4 method for signature 'infusion'
unwrap_treatment(object)

## S4 method for signature 'bolus_wrapper'
unwrap_treatment(object)

## S4 method for signature 'infusion_wrapper'
unwrap_treatment(object)

## S4 method for signature 'treatment'
unwrap_treatment(object)

## S4 method for signature 'arm'
unwrap_treatment(object)

## S4 method for signature 'arms'
unwrap_treatment(object)

## S4 method for signature 'dataset'
unwrap_treatment(object)

Arguments

Value

updated object

Update the number of additional doses (ADDL).

Description

Update the number of additional doses (ADDL).

Usage

update_addl(object, addl, ref = NULL)

## S4 method for signature 'bolus_wrapper,integer,character'
update_addl(object, addl, ref = NULL)

## S4 method for signature 'infusion_wrapper,integer,character'
update_addl(object, addl, ref = NULL)

## S4 method for signature 'bolus,integer,character'
update_addl(object, addl, ref = NULL)

## S4 method for signature 'infusion,integer,character'
update_addl(object, addl, ref = NULL)

## S4 method for signature 'treatment,integer,character'
update_addl(object, addl, ref = NULL)

## S4 method for signature 'arm,integer,character'
update_addl(object, addl, ref = NULL)

## S4 method for signature 'arms,integer,character'
update_addl(object, addl, ref = NULL)

## S4 method for signature 'dataset,integer,character'
update_addl(object, addl, ref = NULL)

Arguments

Value

updated object

Update amount.

Description

Update amount.

Usage

update_amount(object, amount, ref)

## S4 method for signature 'bolus,numeric,character'
update_amount(object, amount, ref)

## S4 method for signature 'infusion,numeric,character'
update_amount(object, amount, ref)

## S4 method for signature 'bolus_wrapper,numeric,character'
update_amount(object, amount, ref)

## S4 method for signature 'infusion_wrapper,numeric,character'
update_amount(object, amount, ref)

## S4 method for signature 'treatment,numeric,character'
update_amount(object, amount, ref)

## S4 method for signature 'arm,numeric,character'
update_amount(object, amount, ref)

## S4 method for signature 'arms,numeric,character'
update_amount(object, amount, ref)

## S4 method for signature 'dataset,numeric,character'
update_amount(object, amount, ref)

Arguments

Value

updated object

Update the inter-dose interval (II).

Description

Update the inter-dose interval (II).

Usage

update_ii(object, ii, ref = NULL)

## S4 method for signature 'bolus_wrapper,numeric,character'
update_ii(object, ii, ref = NULL)

## S4 method for signature 'infusion_wrapper,numeric,character'
update_ii(object, ii, ref = NULL)

## S4 method for signature 'bolus,numeric,character'
update_ii(object, ii, ref = NULL)

## S4 method for signature 'infusion,numeric,character'
update_ii(object, ii, ref = NULL)

## S4 method for signature 'treatment,numeric,character'
update_ii(object, ii, ref = NULL)

## S4 method for signature 'arm,numeric,character'
update_ii(object, ii, ref = NULL)

## S4 method for signature 'arms,numeric,character'
update_ii(object, ii, ref = NULL)

## S4 method for signature 'dataset,numeric,character'
update_ii(object, ii, ref = NULL)

Arguments

Value

updated object

Update the repeat field (argument 'rep' in Bolus and Infusion constructors).

Description

Update the repeat field (argument 'rep' in Bolus and Infusion constructors).

Usage

update_repeat(object, rep, ref = NULL)

## S4 method for signature 'bolus_wrapper,repeated_schedule,character'
update_repeat(object, rep, ref = NULL)

## S4 method for signature 'infusion_wrapper,repeated_schedule,character'
update_repeat(object, rep, ref = NULL)

## S4 method for signature 'bolus,repeated_schedule,character'
update_repeat(object, rep, ref = NULL)

## S4 method for signature 'infusion,repeated_schedule,character'
update_repeat(object, rep, ref = NULL)

## S4 method for signature 'treatment,repeated_schedule,character'
update_repeat(object, rep, ref = NULL)

## S4 method for signature 'arm,repeated_schedule,character'
update_repeat(object, rep, ref = NULL)

## S4 method for signature 'arms,repeated_schedule,character'
update_repeat(object, rep, ref = NULL)

## S4 method for signature 'dataset,repeated_schedule,character'
update_repeat(object, rep, ref = NULL)

Arguments

Value

updated object

Restore a campsis_tbl object

Description

Internal vctrs method to restore the custom 'campsis_tbl' class attributes, specifically copying over the S4 metadata object, after a vector operation.

Usage

## S3 method for class 'campsis_tbl'
vec_restore(x, to, ...)

Arguments

Value

A restored 'campsis_tbl' object with appropriate metadata and classes.

VPC plot.

Description

VPC plot.

Usage

vpcPlot(x, strata = NULL, level = 0.9, alpha = 0.15, facet = TRUE)

Arguments

Value

a ggplot object

VPC plot (S3 generic).

Description

VPC plot (S3 generic).

Usage

vpc_plot(x, ...)

Arguments

Value

a ggplot object

VPC plot for prediction-interval Campsis output.

Description

Plots VPC ribbons (a confidence interval around the median, lower and upper percentiles) from a pi_campsis_tbl, i.e. the output of simulate() with multiple replicates and a PIOutfun. Such data already contains the per-replicate prediction interval in long format (columns replicate, TIME, metric with values "low" / "med" / "up" and value). This method only needs to compute the confidence interval around each percentile across replicates.

Usage

## S3 method for class 'pi_campsis_tbl'
vpc_plot(x, strata = "auto", level = 0.9, alpha = 0.15, facet = TRUE, ...)

Arguments

Details

Stratification is resolved as follows:

• "auto" (default): ARM is used when more than one distinct arm is detected; otherwise SCENARIO is used when more than one distinct scenario is detected; otherwise NULL (no stratification). Only one stratification variable is applied because vpcPlot supports at most one.

• NULL: no stratification.

• named character vector: passed directly to vpcPlot (e.g. c(ARM = "all") or c(SCENARIO = "all")).

Value

a ggplot object

See Also

vpcPlot, PIOutfun

VPC plot for standard Campsis simulation output.

Description

Plots VPC ribbons (a confidence interval around the median, lower and upper percentiles) from a std_campsis_tbl, i.e. the default output of simulate() with multiple replicates and no custom outfun (DefaultOutfun). In this case the data contains all unsummarised individual profiles, so the per-replicate prediction interval is computed first (the lower/median/upper percentiles of variable across individuals within each replicate), before the confidence interval around each percentile is computed across replicates.

Usage

## S3 method for class 'std_campsis_tbl'
vpc_plot(
  x,
  variable = "CONC",
  strata = "auto",
  pi_level = 0.9,
  ci_level = 0.9,
  alpha = 0.15,
  facet = TRUE,
  ...
)

Arguments

Details

Stratification is resolved as follows:

• "auto" (default): ARM is used when more than one distinct arm is detected; otherwise SCENARIO is used when more than one distinct scenario is detected; otherwise NULL (no stratification). Only one stratification variable is applied because vpcPlot supports at most one.

• NULL: no stratification.

• named character vector: passed directly to vpcPlot (e.g. c(ARM = "all") or c(SCENARIO = "all")).

Value

a ggplot object

See Also

vpcPlot, vpc_plot.pi_campsis_tbl

Convert weeks to hours.

Description

Convert weeks to hours.

Usage

weeks(x)

Arguments

Value

numeric vector in hours

Convert pharma years (1 year = 12*4 weeks) to hours.

Description

Convert pharma years (1 year = 12*4 weeks) to hours.

Usage

years(x)

Arguments

Value

numeric vector in hours