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 R packages 'rxode2' and 'mrgsolve'. CAMPSIS provides an abstraction layer over the underlying processes of writing a PK/PD model, assembling a custom dataset and running a simulation. CAMPSIS has a strong dependency to the R package 'campsismod', which allows to read/write a model from/to files and adapt it further on the fly in the R environment. Package 'campsis' allows the user to assemble a dataset in an intuitive manner. Once the user’s dataset is ready, the package is in charge of preparing the simulation, calling 'rxode2' or 'mrgsolve' (at the user's choice) and returning the results, for the given model, dataset and desired simulation settings.
Authors: Nicolas Luyckx [aut, cre]
Maintainer: Nicolas Luyckx <[email protected]>
License: GPL (>= 3)
Version: 1.5.5
Built: 2024-11-26 13:58:30 UTC
Source: CRAN

Help Index

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

applyCompartmentCharacteristics(table, properties)

Arguments

table

current dataset
properties

compartment properties from model

Value

updated dataset

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.

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 = NA,
  f = NULL,
  lag = NULL,
  ii = NULL,
  addl = 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, single integer value
f

fraction of dose amount, distribution
lag

dose lag time, distribution
ii

interdose interval, requires argument 'time' to be a single numeric value
addl

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

Value

a single bolus or a list of boluses

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

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

convertTime(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.

Create a dataset.

Description

Create a dataset.

Usage

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

Arguments

subjects

number of subjects in the default arm
label

label of the default arm, NA by default

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

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 = integer(0))

Arguments

formula

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

compartment numbers where the formula needs to be applied, integer vector. Default is integer(0) (formula applied on all compartments)

Value

a fixed covariate

Filter CAMPSIS output on dosing rows.

Description

Filter CAMPSIS output on dosing rows.

Usage

dosingOnly(x)

Arguments

x

data frame, CAMPSIS output

Value

a data frame with the dosing rows

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

generateIIV(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

generateIIV_(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

getAvailableTimeUnits()

Value

character vector

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

Description

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

Usage

getCovariates(object)

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

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

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

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

Arguments

object

any object

Value

all covariates from object

Get all event-related covariates.

Description

Get all event-related covariates.

Usage

getEventCovariates(object)

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

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

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

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

Arguments

object

any object

Value

all event-related covariates from object

Get all fixed covariates.

Description

Get all fixed covariates.

Usage

getFixedCovariates(object)

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

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

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

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

Arguments

object

any object

Value

all fixed covariates from object

Get all IOV objects.

Description

Get all IOV objects.

Usage

getIOVs(object)

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

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

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

Arguments

object

any object

Value

all IOV's from object

Get all occasions.

Description

Get all occasions.

Usage

getOccasions(object)

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

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

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

Arguments

object

any object

Value

all occasions from object

Get seed for dataset export.

Description

Get seed for dataset export.

Usage

getSeedForDatasetExport(seed, progress)

Arguments

seed

original seed
progress

simulation progress

Value

the seed value used to export the dataset

Get seed for iteration.

Description

Get seed for iteration.

Usage

getSeedForIteration(seed, progress)

Arguments

seed

original seed
progress

simulation progress

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

getSeedForParametersSampling(seed)

Arguments

seed

original seed

Value

the seed value used to sample parameter uncertainty

Get splitting configuration for parallel export.

Description

Get splitting configuration for parallel export.

Usage

getSplittingConfiguration(dataset, hardware)

Arguments

dataset

Campsis dataset to export
hardware

hardware configuration

Value

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

Get all distinct times for the specified object.

Description

Get all distinct times for the specified object.

Usage

getTimes(object)

## S4 method for signature 'observations_set'
getTimes(object)

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

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

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

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

Arguments

object

any object

Value

numeric vector with all unique times, sorted

Get all time-varying covariates.

Description

Get all time-varying covariates.

Usage

getTimeVaryingCovariates(object)

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

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

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

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

Arguments

object

any object

Value

all time-varying covariates from object

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 = NA,
  f = NULL,
  lag = NULL,
  duration = NULL,
  rate = NULL,
  ii = NULL,
  addl = NULL
)

Arguments

time

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

total amount to infuse, numeric
compartment

compartment index, integer
f

fraction of infusion amount, distribution
lag

infusion lag time, distribution
duration

infusion duration, distribution
rate

infusion rate, distribution
ii

interdose interval, requires argument 'time' to be a single numeric value
addl

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

Value

a single infusion or a list of infusions.

Infusion class.

Description

Infusion class.

Slots

duration

infusion duration, distribution

rate

infusion rate, distribution

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, doseNumbers = NULL)

Arguments

colname

name of the column that will be output in dataset
distribution

distribution
doseNumbers

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

Value

an IOV object

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 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

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.

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)

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

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)

Arguments

times

observation times, numeric vector
compartment

compartment index, integer

Value

an observations list

Observations set class.

Description

Observations set class.

Observations class.

Description

Observations class.

Slots

times

observation times, numeric vector

compartment

compartment index, integer

dv

observed values, numeric vector (FOR EXTERNAL USE)

Filter CAMPSIS output on observation rows.

Description

Filter CAMPSIS output on observation rows.

Usage

obsOnly(x)

Arguments

x

data frame, CAMPSIS output

Value

a data frame with the observation rows

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

colname

name of the column that will be output in dataset
values

the occasion numbers, any integer vector
doseNumbers

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 = "scenario"
)

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

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

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

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'.

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

Compute the prediction interval summary over time.

Description

Compute the prediction interval summary over time.

Usage

PI(x, output, scenarios = NULL, level = 0.9, gather = TRUE)

Arguments

x

data frame
output

variable to show, character value
scenarios

scenarios, character vector, NULL is default
level

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

FALSE: med, low & up columns, TRUE: metric column

Value

a summary table

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.

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

Description

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

Usage

retrieveParameterValue(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

RxODE/rxode2 engine class.

Description

RxODE/rxode2 engine class.

Slots

rxode2

logical field to indicate if CAMPSIS should use rxode2 (field set to TRUE) or RxODE (field set to FALSE). Default is TRUE.

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)

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

Arguments

object

generic object
n

number of samples required
...

extra arguments

Value

sampling result

Scatter plot (or X vs Y plot).

Description

Scatter plot (or X vs Y plot).

Usage

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

Arguments

x

data frame
output

the 2 variables to show, character vector
colour

variable(s) to colour
time

the time to look at those 2 variables, if NULL, min time is used (usually 0)

Value

a ggplot object

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 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

Create a list of scenarios.

Description

Create a list of scenarios.

Usage

Scenarios()

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

setLabel(object, x)

## S4 method for signature 'arm,character'
setLabel(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

setSubjects(object, x)

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

## S4 method for signature 'dataset,integer'
setSubjects(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(...)

Arguments

...

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

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

setupPlanDefault(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

setupPlanSequential()

Value

nothing

Shaded plot (or prediction interval plot).

Description

Shaded plot (or prediction interval plot).

Usage

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

Arguments

x

data frame
output

variable to show
colour

variable(s) to colour
strat_extra

variable(s) to stratify, but not to colour (useful for use with facet_wrap)
level

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

alpha parameter (transparency) given to geom_ribbon

Value

a ggplot object

Simulate function.

Description

Simulate function.

Usage

simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   dataset,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   tbl_df,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   data.frame,
##   character,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   tbl_df,
##   rxode_engine,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  dosing = FALSE,
  settings = NULL
)

## S4 method for signature 
## 'campsis_model,
##   tbl_df,
##   mrgsolve_engine,
##   events,
##   scenarios,
##   function,
##   character,
##   output_function,
##   integer,
##   integer,
##   logical,
##   simulation_settings'
simulate(
  model,
  dataset,
  dest = NULL,
  events = NULL,
  scenarios = NULL,
  tablefun = NULL,
  outvars = NULL,
  outfun = NULL,
  seed = NULL,
  replicates = 1,
  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.

Arguments

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

hardware

hardware settings object

solver

solver settings object

nocb

NOCB settings object

declare

declare settings (mrgsolve only)

progress

progress 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 RxODE/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 RxODE/rxode2 only: 'liblsoda' (default), 'lsoda', 'dop853', 'indLin'. Mrgsolve's method is always 'lsoda'.

Spaghetti plot.

Description

Spaghetti plot.

Usage

spaghettiPlot(x, output, colour = NULL)

Arguments

x

data frame
output

variable to show
colour

variable(s) to colour

Value

plot

Standardise time to hours.

Description

Standardise time to hours.

Usage

standardiseTime(x, unit)

Arguments

x

numeric time vector
unit

unit of x, single character value

Value

numeric vector with the times converted to hours

Time-varying covariate class.

Description

Time-varying covariate class.

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

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.

Treatment class.

Description

Treatment class.

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

Create an uniform distribution.

Description

Create an uniform distribution.

Usage

UniformDistribution(min, max)

Arguments

min

min value
max

max value

Value

an uniform distribution

Compute 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

Compute 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

VPC(x, scenarios = NULL, level = 0.9)

Arguments

x

data frame
scenarios

scenarios, character vector, NULL is default
level

PI level, default is 0.9 (90% PI)

Value

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

VPC plot.

Description

VPC plot.

Usage

vpcPlot(x, scenarios = NULL, level = 0.9, alpha = 0.15)

Arguments

x

data frame, output of CAMPSIS with replicates
scenarios

scenarios, character vector, NULL is default
level

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

alpha parameter (transparency) given to geom_ribbon

Value

a ggplot object

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