simulx: Simulation of mixed effects models and longitudinal data
In mlxR: Simulation of Longitudinal Data

Description Usage Arguments Details Value Examples

Compute predictions and sample data from Mlxtran and R models

simulx(
  model = NULL,
  parameter = NULL,
  output = NULL,
  treatment = NULL,
  regressor = NULL,
  varlevel = NULL,
  group = NULL,
  data = NULL,
  project = NULL,
  nrep = 1,
  npop = NULL,
  fim = NULL,
  result.folder = NULL,
  result.file = NULL,
  stat.f = "statmlx",
  addlines = NULL,
  settings = NULL
)

`model`	a `Mlxtran`, or `R` model used for the simulation
`parameter`	a vector of parameters with their names and values
`output`	a list (or list of lists) with fields: `name`: a vector of output names `time`: a vector of times (only for the longitudinal outputs) `lloq`: lower limit of quantification (only for the longitudinal outputs) `uloq`: upper limit of quantification (only for the longitudinal outputs) `limit`: lower bound of the censoring interval (only for the longitudinal outputs)
`treatment`	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), `type` : the type of input (default=1), `target` : the target compartment (default=NULL).
`regressor`	a list, or a list of lists, with fields `name` : a vector of regressor names, `time` : a vector of times, `value` : a vector of values.
`varlevel`	(IOV supported by mlxR >= 3.2.2) a list (or a dataframe) with fields: `name` : name of the variable which defines the occasions, `time` : a vector of times (beginnings of occasions) ,
`group`	a list, or a list of lists, with fields: `size` : size of the group (default=1), `level` : level(s) of randomization, `parameter` : if different parameters per group are defined, `output` : if different outputs per group are defined, `treatment` : if different treatements per group are defined, `regressor` : if different regression variables per group are defined.
`data`	a list (output of simulx when settings$data.in==TRUE)
`project`	the name of a Monolix project
`nrep`	number of replicates
`npop`	number of population parameters to draw randomly
`fim`	a string with the Fisher Information Matrix to be used
`result.folder`	the name of the folder where the outputs of simulx should be stored
`result.file`	the name of the single file where the outputs of simulx should be saved
`stat.f`	a R function for computing some summary (mean, quantiles, survival,...) of the simulated data. Default = "statmlx".
`addlines`	a list with fields: `section`: a string (default = "[LONGITUDINAL]"), `block`: a string (default = "EQUATION:"), `formula`: string, or vector of strings, to be inserted .
`settings`	a list of optional settings `seed` : initialization of the random number generator (integer), `load.design` : TRUE/FALSE (if load.design is not defined, a test is automatically performed to check if a new design has been defined), `data.in` : TRUE/FALSE (default=FALSE) `id.out` : add columns id (when N=1) and group (when #group=1), TRUE/FALSE (default=FALSE) `kw.max` : maximum number of trials for generating a positive definite covariance matrix (default = 100) `sep` : the field separator character (default = ",") `digits` : number of decimal digits in output files (default = 5) `disp.iter` : TRUE/FALSE (default = FALSE) display replicate and population numbers `replacement` : TRUE/FALSE (default = FALSE) sample id's with/without replacement `out.trt` : TRUE/FALSE (default = TRUE) output of simulx includes treatment `format.original` : TRUE/FALSE (default = FALSE) with a Monolix project, write data in result.file using the original format of the data file

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, or R. Mlxtran models are automatically converted into C++ codes, compiled on the fly and linked to R using the Rcpp 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.webpopix.org for more details.

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

## 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))
res <- simulx(model     = 'model/home.txt', 
              parameter = c(A=100, k_pop=6, omega=0.3, c=10, a=2), 
              output    = list(f,y,"k"),
              group     = list(size=4, level='individual'))

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)