R: Simulation of mixed effects models and longitudinal data

simulx {RsSimulx}

R Documentation

Simulation of mixed effects models and longitudinal data

Description

Compute predictions and sample data from Mlxtran and R models

Usage

simulx(
  model = NULL,
  parameter = NULL,
  covariate = NULL,
  output = NULL,
  treatment = NULL,
  regressor = NULL,
  occasion = NULL,
  varlevel = NULL,
  group = NULL,
  project = NULL,
  nrep = 1,
  npop = NULL,
  fim = NULL,
  saveSmlxProject = NULL,
  result.file = NULL,
  addlines = NULL,
  settings = NULL
)

Arguments

`model`	a `Mlxtran` model used for the simulation. It can be a text file or an outut of the inLine function.
`parameter`	One of a vector of parameters with their names and values, a dataframe with parameters defined for each id or each pop a string, path to a data frame (csv or txt file) a string corresponding to the parameter elements automatically generated by monolix (only when simulation is based on a Monolix project). One of the following mlx parameter elements: "mlx_Pop", "mlx_PopUncertainSA", "mlx_PopUncertainLin", "mlx_PopIndiv", "mlx_PopIndivCov","mlx_CondMean", "mlx_EBEs","mlx_CondDistSample"
`covariate`	One of a vector of covariates with their names and values. a dataframe with covariates defined for each id a string, path to a data frame (csv or txt file) a string corresponding to the covariate elements automatically generated by monolix (only when simulation is based on a Monolix project). One of the following mlx covariate elements: "mlx_Cov" and "mlx_CovDist"
`output`	output or list of outputs. An output can be defined by a string corresponding to the output elements automatically generated by monolix (only when simulation is based on a Monolix project) - the format is mlx_nameofoutput. a list with fields `name`: a vector of output names `time`: a vector of times a dataframe with columns id, time (columns lloq, uloq, limit are optional) a string, path to a data frame (csv or txt file) `lloq`: lower limit of quantification (when time is a vector of times) `uloq`: upper limit of quantification (when time is a vector of times) `limit`: lower bound of the censoring interval (when time is a vector of times)
`treatment`	treatment or list of treatments. A treatment can be defined by A list with fields `time` : a vector of input times, `amount` : a scalar or a vector of amounts, `rate` : a scalar or a vector of infusion rates (default=`Inf`), `tinf` : a scalar or a vector of infusion times (default=0), `washout` : a scalar or a vector of boolean (default=F), `adm` : (or type) the administration type (default=1), `repeats` : the treatment cycle (optional), a vector with fields `cycleDuration` : the duration of a cycle, `NumberOfRepetitions` : the number of cycle repetition `probaMissDose`: the probability to miss each dose (optional). a dataframe with treatments defined for each id a string, path to a data frame (csv or txt file) a string corresponding to the treatment elements automatically generated by monolix (only when simulation is based on a Monolix project) - for example mlx_Adm1.
`regressor`	treatment or list of treatments. A treatment can be defined by A list with fields `name` : a vector of regressor names, `time` : a vector of times, `value` : a vector of values. A dataframe with columns id, time, and regressor values a string, path to a data frame (csv or txt file)
`occasion`	An occasion can be defined by A list with fields `name` : name of the variable which defines the occasions, `time` : a vector of times (beginnings of occasions) A dataframe with columns id, time, occasion a string, path to a data frame (csv or txt file) "none", to delete an occasion structure
`varlevel`	deprecated, use occasion instead.
`group`	a list, or a list of lists, with fields: `size` : size of the group (default=1), `parameter` : if different parameters per group are defined, `covariate` : if different covariates per group are defined, `output` : if different outputs per group are defined, `treatment` : if different treatments per group are defined, `regressor` : if different regression variables per group are defined. "level" field is not supported anymore in RsSimulx.
`project`	the name of a Monolix project
`nrep`	Samples with or without uncertainty depending which element is given as "parameter".
`npop`	deprecated, Set parameter = "mlx_popUncertainSA" or "mlx_popUncertainLin" instead.
`fim`	deprecated, Set parameter = "mlx_popUncertainSA" or "mlx_popUncertainLin" instead.
`saveSmlxProject`	If specified, smlx project will be save in the path location (by default smlx project is not saved)
`result.file`	deprecated
`addlines`	a list with fields: `formula`: string, or vector of strings, to be inserted . "section", "block" field are not supported anymore in RsSimulx. You only need to specify a formula. The additional lines will be added in a new section EQUATION.
`settings`	a list of optional settings `seed`: initialization of the random number generator (integer) (by default a random seed will be generated) `id.out`: add (TRUE) / remove (FALSE) columns id and group when only one element (N = 1 or group = 1) (default=FALSE) `kw.max`: deprecated. `replacement` : deprecated, use samplingMethod instead `samplingMethod`: str, Sampling method used for the simulation. One of "keepOrder", "withReplacement", "withoutReplacement" (default "keepOrder") `out.trt`: TRUE/FALSE (default = TRUE) output of simulx includes treatment `out.reg`: TRUE/FALSE (default = TRUE) output of simulx includes regressors `sharedIds`: Vector of Elements that share ids. Available types are "covariate", "output", "treatment", "regressor", "population", "individual" (default c()) `sameIndividualsAmongGroups`: boolean, if True same individuals will be simulated among all groups (default False) `exportData`: boolean, if True and if a path to save the smlx project (saveSmlxProject) is specified, export the simulated dataset (smlx project directory/Simulation/simulatedData.txt) (default False) `regressorInterpolationMethod`: interpolation method for missing regressors. One of "lastCarriedForward" (default), "linearInterpolation"

Details

simulx takes advantage of the modularity of hierarchical models for simulating different components of a model: models for population parameters, individual covariates, individual parameters and longitudinal data.

Furthermore, simulx allows to draw different types of longitudinal data, including continuous, count, categorical, and time-to-event data.

The models are encoded using either the model coding language ‘⁠Mlxtran⁠’. ‘⁠Mlxtran⁠’ models are automatically converted into C++ codes, compiled on the fly and linked to R using the ‘⁠RJSONIO⁠’ package. That allows one to implement very easily complex models and to take advantage of the numerical sovers used by the C++ ‘⁠mlxLibrary⁠’.

See http://simulx.lixoft.com for more details.

Value

A list of data frames. Each data frame is an output of simulx

Examples

## Not run: 
myModel <- inlineModel("
[LONGITUDINAL]
input = {A, k, c, a}
EQUATION:
t0    = 0
f_0   = A
ddt_f = -k*f/(c+f)
DEFINITION:
y = {distribution=normal, prediction=f, sd=a}
[INDIVIDUAL]
input = {k_pop, omega}
DEFINITION:
k = {distribution=lognormal, prediction=k_pop, sd=omega}
")

f <- list(name='f', time=seq(0, 30, by=0.1))
y <- list(name='y', time=seq(0, 30, by=2))
parameter <- c(A=100, k_pop=6, omega=0.3, c=10, a=2)

res <- simulx(model     = myModel,
              parameter = parameter,
              occasion  = data.frame(time=c(0, 0), occ=c(1, 2)),
              output    = list(f,y),
              group     = list(size=4),
              saveSmlxProject = "./project.smlx")

res <- simulx(model     = myModel,
              parameter = parameter,
              occasion  = data.frame(time = c(0, 0, 0, 0),
                                     occ1 = c(1, 1, 2, 2),
                                     occ2 = c(1, 2, 3, 4)),
              output    = list(f,y),
              group     = list(size=4))
              
res <- simulx(model     = myModel,
              parameter = parameter,
              output    = list(f,y),
              group     = list(size=4))

plot(ggplotmlx() + geom_line(data=res$f, aes(x=time, y=f, colour=id)) +
     geom_point(data=res$y, aes(x=time, y=y, colour=id)))
print(res$parameter)


## End(Not run)

[Package RsSimulx version 2024.1 Index]