Package 'campsis'

Title: Generic PK/PD Simulation Platform Campsis
Description: A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on the R packages 'rxode2' and 'mrgsolve'. Campsis provides an abstraction layer over the underlying processes of defining a PK/PD model, assembling a custom dataset and running a simulation. The package has a strong dependency on the R package 'campsismod', which allows models to be read from and written to files, including through a JSON-based interface, and to be adapted further on the fly in the R environment. In addition, 'campsis' allows users to assemble datasets in an intuitive manner, including via a JSON-based interface to import Campsis datasets defined using formal JSON schemas distributed with the package. Once the dataset is ready, the package prepares the simulation, calls 'rxode2' or 'mrgsolve' (at the user's choice), and returns the results for the given model, dataset and desired simulation settings. The package itself is licensed under the GPL (>= 3); the JSON schema files shipped in inst/extdata are licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0).
Authors: Nicolas Luyckx [aut, cre], Calvagone [cph]
Maintainer: Nicolas Luyckx <[email protected]>
License: GPL (>= 3)
Version: 1.9.3
Built: 2026-10-20 10:25:12 UTC
Source: https://github.com/campsisverse/campsis

Help Index

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

object

any object
action

action to apply

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

table

current dataset
properties

compartment properties from model

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

x

Campsis simulation results
outfun

output function(s), an 'outfun' or 'outfuns' object
level

level at which the output function is applied, 'replicate' by default
...

extra arguments transmitted automatically by Campsis (e.g. 'replicate' for the replicate number)

Value

the updated Campsis results

Create a treatment arm.

Description

Create a treatment arm.

Usage

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

Arguments

id

unique identifier for this arm (available trough dataset), integer. If NA (default), this identifier is auto-incremented.
subjects

number of subjects in arm, integer
label

arm label, single character string. If set, this label will be output in the ARM column of Campsis instead of the identifier.

Value

an arm

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

x

time sequence object

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

x

time vector object

Value

a numeric vector

Binomial distribution class.

Description

Binomial distribution class.

Binomial distribution.

Description

Binomial distribution.

Usage

BinomialDistribution(trials, prob)

Arguments

trials

number of Bernoulli trials per observation (=subject), integer
prob

probability of success for each trial

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

time

treatment time(s), numeric value or vector. First treatment time if used together with ii and addl.
amount

amount to give as bolus, single numeric value
compartment

compartment index or name to give the bolus(es). A vector of integers or names can be used for a complex model administration.
f

fraction of dose amount, list of distributions (one per compartment)
lag

dose lag time, list of distributions (one per compartment)
ii

inter-dose interval, requires argument 'time' to be a single numeric value
addl

number of additional doses, requires argument 'time' to be a single integer value
wrap

if TRUE, the bolus wrapper will be stored as is in the dataset, otherwise, it will be split into a list of boluses distinct in time. Default is TRUE.
ref

any reference name used to identify this bolus, single character value
rep

repeat the base dosing schedule several times, a 'repeated schedule' object is expected. Default is NULL (no repetition).

Value

a single bolus or a list of boluses

Bolus wrapper class.

Description

Bolus wrapper class.

Bolus class.

Description

Bolus class.

Create a bootstrap object.

Description

Create a bootstrap object.

Usage

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

Arguments

data

data frame to be bootstrapped. It must have a unique identifier column named according to the specified argument 'id' (default value is 'BS_ID'). Other columns are covariates to bootstrap. They must all be numeric. Whatever the configuration of the bootstrap, these covariates are always read row by row and belong to a same individual.
id

unique identifier column name in data
replacement

values can be reused or not when drawn, logical
random

values are drawn randomly, logical
export_id

tell Campsis if the identifier 'BS_ID' must be output or not, logical

Value

a bootstrap object

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

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

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

data

values to draw, numeric vector
replacement

values can be reused or not, logical
random

values are drawn randomly, logical

Value

a bootstrap distribution

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)

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

x

data frame
variable

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

named vector with the strata to use, default is c(SCENARIO="all", ARM="all").
level

PI level, default is 0.9 (90% PI)

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

x

data frame
variable

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

named vector with the strata to use, default is c(SCENARIO="all", ARM="all").
stats

character vector of statistics to compute. Supported: "median", "mean", or percentiles like "p5", "p95", "p2.5", "p97.5".

Value

a summary table in long format

Constant distribution class.

Description

Constant distribution class.

Slots

value

covariate value, single numeric value

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

covariate value, single numeric value

Value

a constant distribution (same value for all samples)

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

x

numeric time vector
from

unit of x, single character value
to

destination unit, single character value

Value

numeric vector with the converted times

Create a non time-varying (fixed) covariate.

Description

Create a non time-varying (fixed) covariate.

Usage

Covariate(name, distribution)

Arguments

name

covariate name, single character value
distribution

covariate distribution

Value

a fixed covariate

Covariate class.

Description

Covariate class.

Slots

name

covariate name, single character value

distribution

covariate distribution

Covariates class.

Description

Covariates class.

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

Cyclic schedule constructor.

Description

Cyclic schedule constructor.

Usage

CyclicSchedule(duration, repetitions)

Arguments

duration

duration of the cycle, numeric value
repetitions

number of additional repetitions to the base pattern, integer value

Value

a cyclic schedule

Create a dataset.

Description

Create a dataset.

Usage

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

Arguments

subjects

number of subjects in the default arm
label

label of the default arm, NA by default
json

path to JSON dataset file or JSON content in string form

Value

a dataset

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)

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

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

defDepotCmt

default depot compartment, integer
defObsCmt

default observation compartment, integer
exportTSLD

export column TSLD (time since last dose), logical
exportTDOS

export column TDOS (time of last dose), logical
timeUnitDataset

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

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

Value

a dataset configuration

Convert days to hours.

Description

Convert days to hours.

Usage

days(x)

Arguments

x

numeric vector in days

Value

numeric vector in hours

Create declare settings.

Description

Create declare settings.

Usage

Declare(variables = character(0))

Arguments

variables

uninitialized variables to be declared, only needed with mrgsolve

Value

Declare settings

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

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

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 (or NULL for auto-generated seed)
replicates

number of replicates, integer. Default is 1.
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

Value

default settings

Discrete distribution class.

Description

Discrete distribution class.

Discrete distribution.

Description

Discrete distribution.

Usage

DiscreteDistribution(x, prob, replace = TRUE)

Arguments

x

vector of one or more integers from which to choose
prob

a vector of probability weights for obtaining the elements of the vector being sampled
replace

should sampling be with replacement, default is TRUE

Value

a discrete distribution

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.

Create a dose adaptation.

Description

Create a dose adaptation.

Usage

DoseAdaptation(formula, compartments = NULL)

Arguments

formula

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

compartment indexes or names where the formula needs to be applied, integer or character vector. Default is NULL (formula applied on all compartments)

Value

a fixed covariate

Filter Campsis output on dosing rows.

Description

Filter Campsis output on dosing rows.

Usage

dosing_only(x)

Arguments

x

data frame, Campsis output

Value

a data frame with the dosing rows

Dosing schedule class.

Description

Dosing schedule class.

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

model

model
omega

corresponding THETA name, character

Value

an ETA distribution

Create an interruption event.

Description

Create an interruption event.

Usage

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

Arguments

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

Value

an event definition

Event covariate class.

Description

Event covariate class.

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

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

name

covariate name, character
distribution

covariate distribution at time 0

Value

a time-varying covariate

Create a list of interruption events.

Description

Create a list of interruption events.

Usage

Events()

Value

a events object

Events class.

Description

Events class.

Fixed covariate class.

Description

Fixed covariate class.

Fixed distribution class.

Description

Fixed distribution class.

Slots

values

covariate values, numeric vector (1 value per sample)

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

values

covariate values, numeric vector (1 value per sample)

Value

a fixed distribution (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))

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

fun

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

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

Value

a function distribution

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

model

Campsis model
n

number of subjects
offset

if specified, resulting ID will be ID + offset

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

omega

omega matrix
n

number of subjects

Value

IIV data frame

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

name

option to search
default

default value if option not found

Value

option value

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

object

any object

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

object

any object

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

object

any object

Value

all fixed covariates from object

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

object

any object

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

object

any object

Value

all occasions from object

Get splitting configuration for parallel export.

Description

Get splitting configuration for parallel export.

Usage

get_splitting_configuration(dataset, hardware)

Arguments

dataset

Campsis dataset to export
hardware

hardware configuration

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

object

any object

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

object

any object
...

extra arguments like 'doseTimes' in observations or 'unwrap' in treatment
unwrap

unwrap treatment before accessing the times, default value is TRUE
doseTimes

times of the doses, only needed if a [DosingSchedule()] is referred to

Value

numeric vector with all unique times, sorted

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

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, if not set (i.e. =NULL), plan will be setup automatically if the number of CPU's > 1.

Value

hardware settings

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

x

numeric vector in hours

Value

numeric vector in hours

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

time

treatment time(s), numeric value or vector. First treatment time if used together with ii and addl.
amount

amount to infuse, single numeric value
compartment

compartment index or name to give the infusion(s). A vector of integers or names can be used for a complex model administration.
f

fraction of infusion amount, list of distributions (one per compartment)
lag

infusion lag time, , list of distributions (one per compartment)
duration

infusion duration, list of distributions (one per compartment)
rate

infusion rate, list of distributions (one per compartment)
ii

inter-dose interval, requires argument 'time' to be a single numeric value
addl

number of additional doses, requires argument 'time' to be a single integer value
wrap

if TRUE, the infusion wrapper will be stored as is in the dataset, otherwise, it will be split into a list of infusions distinct in time. Default is TRUE.
ref

any reference name used to identify this infusion, single character value
rep

repeat the base dosing schedule several times, a 'repeated schedule' object is expected. Default is NULL (no repetition).

Value

a single infusion or a list of infusions.

Infusion wrapper class.

Description

Infusion wrapper class.

Infusion class.

Description

Infusion class.

Slots

duration

infusion duration, distribution list

rate

infusion rate, distribution list

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

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 = NULL, dose_numbers = NULL, omega_ref = NULL)

Arguments

colname

name of the column that will be output in dataset
distribution

distribution
dose_numbers

dose numbers, if provided, IOV is generated at these doses only. By default, IOV is generated for all doses.
omega_ref

name of the OMEGA (e.g. 'IOV' without the 'OMEGA_')

Value

an IOV object

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

omega_ref

name of the OMEGA (e.g. 'IOV' without the 'OMEGA_')

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

x

arm

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

x

schedule object

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

x

dataset

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

x

schedule object

Value

a number

Log normal distribution class.

Description

Log normal distribution class.

Create a log normal distribution.

Description

Create a log normal distribution.

Usage

LogNormalDistribution(meanlog, sdlog)

Arguments

meanlog

mean value of distribution in log domain
sdlog

standard deviation of distribution in log domain

Value

a log normal distribution

Convert minutes to hours.

Description

Convert minutes to hours.

Usage

minutes(x)

Arguments

x

numeric vector in minutes

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

x

numeric vector in months

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'

Create a NCA table output function

Description

Create a NCA table output function

Usage

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

Arguments

table

NCA table from campsisnca, object, path to JSON, or JSON element
export_type

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

name of the output function. Default is 'default_nca_table'.

Value

a stats_outfun object

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

Create NOCB settings.

Description

Create NOCB settings.

Usage

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

Arguments

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)

Value

NOCB settings

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.

Create a normal distribution.

Description

Create a normal distribution.

Usage

NormalDistribution(mean, sd)

Arguments

mean

mean value of distribution
sd

standard deviation of distribution

Value

a normal distribution

Filter Campsis output on observation rows.

Description

Filter Campsis output on observation rows.

Usage

obs_only(x)

Arguments

x

data frame, Campsis output

Value

a data frame with the observation rows

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

times

observation times, numeric vector
compartment

compartment index (integer) or name (character)
rep

repetition schedule

Value

an observations list

Observations set class.

Description

Observations set class.

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

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, dose_numbers)

Arguments

colname

name of the column that will be output in dataset
values

the occasion numbers, any integer vector
dose_numbers

the related dose numbers, any integer vector of same length as 'values'

Value

occasion object

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.

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

fun

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

extra arguments, named list
packages

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

where to apply the output function, only 'replicate' is allowed since Campsis v1.9.0
name

name of the output function. Default is 'custom'.

Value

an output function

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

Create a collection of output functions.

Description

Create a collection of output functions.

Usage

Outfuns()

Value

an output_functions object

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

Description

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

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

model

model
theta

corresponding THETA name, character
omega

corresponding OMEGA name, character, NULL if not defined

Value

a parameter distribution

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)

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

variable

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

named vector with the strata to use, default is c(SCENARIO="all", ARM="all")
level

PI level, default is 0.9 (90% PI)
name

name of the output function. Default is 'pi_<variable>_<level>pc'.

Value

a pi_outfun object

Create progress settings.

Description

Create progress settings.

Usage

Progress(tick_slice = TRUE)

Arguments

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.

Value

progress settings

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.

'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

x

object to repeat the schedule
schedule

initial times vector

Value

resulting times vector

'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

times

times at which the original schedule must be repeated, numeric vector

Value

a 'repeat-at' schedule

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.

Create a replace action.

Description

Create a replace action.

Usage

ReplaceAction(object)

Arguments

object

replacement object

Value

a replace action

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

model

model
paramName

parameter name
default

default value if not found
mandatory

must be in model or not

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

object

generic object
n

number of samples required
...

extra arguments

Value

sampling result

Scatter plot (S3 generic).

Description

Scatter plot (S3 generic).

Usage

scatter_plot(x, ...)

Arguments

x

a Campsis output object
...

additional arguments passed to the method

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

x

a std_campsis_tbl object
variable

character vector of length 1 or 2 giving the column name(s) to plot. Defaults to "CONC". An informative error is raised when any column is absent.
colour

stratification for point colour. One of "auto", NULL, or a character vector of column names. See Details.
time

numeric vector of time point(s) to filter to before plotting. When NULL (default) the minimum time in the data is used (typically time 0), which is useful for plotting individual parameters. Forwarded to scatterPlot.
...

additional arguments (currently unused, reserved for future use)

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

Create an scenario.

Description

Create an scenario.

Usage

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

Arguments

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

Value

a new scenario

Scenario action class.

Description

Scenario action class.

Scenario actions class.

Description

Scenario actions class.

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

enabled

is the scenario enabled in the simulation, logical value

Create a list of scenarios.

Description

Create a list of scenarios.

Usage

Scenarios(json = NULL)

Arguments

json

path to JSON scenarios file or JSON content in string form

Value

a scenarios object

Scenarios class.

Description

Scenarios class.

Convert seconds to hours.

Description

Convert seconds to hours.

Usage

seconds(x)

Arguments

x

numeric vector in seconds

Value

numeric vector in hours

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

object

any object that has a label
x

the new label

Value

the updated object

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

object

any object
x

the new number of subjects

Value

the updated object

Create advanced simulation settings.

Description

Create advanced simulation settings.

Usage

Settings(..., json = NULL)

Arguments

...

any user-required settings: see ?Hardware, ?Solver, ?NOCB, ?Declare, ?Progress or ?AutoReplicationSettings
json

path to JSON settings file or JSON content in string form

Value

advanced simulation settings

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

object

simulation or hardware settings

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 / prediction interval plot (S3 generic).

Description

Shaded plot / prediction interval plot (S3 generic).

Usage

shaded_plot(x, ...)

Arguments

x

a Campsis output object
...

additional arguments passed to the method

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

x

a std_campsis_tbl object
variable

name of the column to summarise on the y-axis. Defaults to "CONC". An informative error is raised when the column is absent.
colour

stratification for line and ribbon colour. One of "auto", NULL, or a character vector of column names. See Details.
strat_extra

additional column name(s) used for stratification in the prediction interval computation but not mapped to colour — useful when combined with facet_wrap(). Forwarded to shadedPlot. Default is NULL.
level

prediction interval level. Default is 0.90 (90% PI).
alpha

transparency of the ribbon. Default is 0.25.
...

additional arguments (currently unused, reserved for future use)

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

model

generic Campsis model
dataset

Campsis dataset or 2-dimensional table
dest

destination simulation engine, default is 'RxODE'
events

interruption events
scenarios

list of scenarios to be simulated
tablefun

function or lambda formula to apply on exported 2-dimensional dataset
outvars

variables to output in resulting dataframe
outfun

an output function to apply on the simulation results. Type ?Outfun for more info.
seed

seed value
replicates

number of replicates, default is 1
dosing

output dosing information, default is FALSE
settings

advanced simulation settings

Value

dataframe with all 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

Create a simulation progress object.

Description

Create a simulation progress object.

Usage

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

Arguments

replicates

total number of replicates to simulate
scenarios

total number of scenarios to simulate
progressor

progressr progressor
hardware

hardware settings

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

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'.

Value

solver 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 (S3 generic).

Description

Spaghetti plot (S3 generic).

Usage

spaghetti_plot(x, ...)

Arguments

x

a Campsis output object
...

additional arguments passed to the method

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

x

a std_campsis_tbl object
variable

name of the column to plot on the y-axis. Defaults to "CONC". An informative error is raised when the column is absent.
colour

stratification for line colour. One of "auto", NULL, or a character vector of column names. See Details.
...

additional arguments (currently unused, reserved for future use)

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

Standardise time to hours.

Description

Standardise time to hours.

Usage

standardise_time(x, unit)

Arguments

x

numeric time vector
unit

unit of x, single character value

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

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

variable

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

named vector with the strata to use, default is c(SCENARIO="all", ARM="all")
stats

character vector of statistics to compute. Supported: "median", "mean", or percentiles like "p5", "p95", "p2.5", "p97.5". Default is c("p5", "median", "p95").
name

name of the output function. Default is 'stats_<variable>'.

Value

a stats_outfun object

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.

Instantiate a new time sequence.

Description

Instantiate a new time sequence.

Usage

TimeSequence(start, end, by)

Arguments

start

starting value
end

maximal value
by

increment of the sequence

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

name

covariate name, character
table

data.frame, must contain the mandatory columns 'TIME' and 'VALUE'. An 'ID' column may also be specified. In that case, ID's between 1 and the max number of subjects in the dataset/arm can be used. All ID's must have a VALUE defined for TIME 0.

Value

a time-varying covariate

Instantiate a new time vector

Description

Instantiate a new time vector

Usage

TimeVector(x)

Arguments

x

time vector, numeric

Value

a time vector

Treatment IOV's class.

Description

Treatment IOV's class.

Treatment class.

Description

Treatment 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.

Create an uniform distribution.

Description

Create an uniform distribution.

Usage

UniformDistribution(min, max)

Arguments

min

min value
max

max value

Value

an uniform distribution

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

object

any object

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

object

generic object
addl

new number of additional doses
ref

reference treatment name

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

object

generic object
amount

new amount
ref

reference treatment name

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

object

generic object
ii

new inter-dose interval
ref

reference treatment name

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

object

generic object
rep

repeated dosing schedule (definition) object
ref

reference treatment name

Value

updated object

VPC plot (S3 generic).

Description

VPC plot (S3 generic).

Usage

vpc_plot(x, ...)

Arguments

x

a Campsis output object
...

additional arguments passed to the method

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

x

a pi_campsis_tbl object with a replicate column
strata

stratification for the VPC. One of "auto", NULL, or a named character vector of length 1. See Details.
level

confidence interval level for the ribbons around each percentile. Default is 0.90 (90% CI).
alpha

transparency of the ribbons. Default is 0.15.
facet

how the stratification variable is displayed when present. TRUE (default) draws one panel per stratum; FALSE overlays the strata in a single panel and maps the stratum to the ribbon fill colour. Forwarded to vpcPlot.
...

additional arguments (currently unused, reserved for future use)

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

x

a std_campsis_tbl object with a replicate column
variable

name of the column to summarise on the y-axis. Defaults to "CONC". An informative error is raised when the column is absent.
strata

stratification for the VPC. One of "auto", NULL, or a named character vector of length 1. See Details.
pi_level

prediction interval level, i.e. the percentiles of the data computed within each replicate. Default is 0.90 (90% PI, i.e. the 5th, 50th and 95th percentiles).
ci_level

confidence interval level for the ribbons around each percentile across replicates. Default is 0.90 (90% CI).
alpha

transparency of the ribbons. Default is 0.15.
facet

how the stratification variable is displayed when present. TRUE (default) draws one panel per stratum; FALSE overlays the strata in a single panel and maps the stratum to the ribbon fill colour. Forwarded to vpcPlot.
...

additional arguments (currently unused, reserved for future use)

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

x

numeric vector in weeks

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

x

numeric vector in years

Value

numeric vector in hours