This takes the uncertainty in the model parameter estimates and to simulate a number of theoretical studies. Each study simulates a realization of the parameters from the uncertainty in the fixed parameter estimates. In addition the omega and sigma matrices are simulated from the uncertainty in the Omega/Sigma matrices based on the number of subjects and observations the model was based on.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  nlmixrSim(object, ...)
## S3 method for class 'nlmixrFitData'
rxSolve(object, params = NULL, events = NULL,
inits = NULL, scale = NULL, covs = NULL, method = c("liblsoda",
"lsoda", "dop853"), transitAbs = NULL, atol = 1e06, rtol = 1e04,
maxsteps = 5000L, hmin = 0L, hmax = NULL, hini = 0L,
maxordn = 12L, maxords = 5L, ..., cores,
covsInterpolation = c("linear", "locf", "nocb", "midpoint"),
addCov = FALSE, matrix = FALSE, sigma = NULL, sigmaDf = NULL,
nCoresRV = 1L, sigmaIsChol = FALSE, nDisplayProgress = 10000L,
amountUnits = NA_character_, timeUnits = "hours", stiff,
theta = NULL, eta = NULL, addDosing = FALSE,
updateObject = FALSE, doSolve = TRUE, omega = NULL,
omegaDf = NULL, omegaIsChol = FALSE, nSub = 1L, thetaMat = NULL,
thetaDf = NULL, thetaIsChol = FALSE, nStud = 1L, dfSub = 0,
dfObs = 0, returnType = c("rxSolve", "matrix", "data.frame"),
seed = NULL, nsim = NULL)
## S3 method for class 'nlmixrFitData'
simulate(object, nsim = 1, seed = NULL, ...)
## S3 method for class 'nlmixrFitData'
solve(a, b, ...)

object 
nlmixr object 
... 
Other arguments sent to 
params 
a numeric named vector with values for every parameter in the ODE system; the names must correspond to the parameter identifiers used in the ODE specification; 
events 
an 
inits 
a vector of initial values of the state variables (e.g., amounts in each compartment), and the order in this vector must be the same as the state variables (e.g., PK/PD compartments); 
scale 
a numeric named vector with scaling for ode
parameters of the system. The names must correstond to the
parameter identifiers in the ODE specification. Each of the
ODE variables will be divided by the scaling factor. For
example 
covs 
a matrix or dataframe the same number of rows as the
sampling points defined in the events 
method 
The method for solving ODEs. Currently this supports:

transitAbs 
boolean indicating if this is a transit compartment absorption 
atol 
a numeric absolute tolerance (1e8 by default) used by the ODE solver to determine if a good solution has been achieved; This is also used in the solved linear model to check if prior doses do not add anything to the solution. 
rtol 
a numeric relative tolerance (1e6 by default) used by the ODE solver to determine if a good solution has been achieved. This is also used in the solved linear model to check if prior doses do not add anything to the solution. 
maxsteps 
maximum number of (internally defined) steps allowed during one call to the solver. (5000 by default) 
hmin 
The minimum absolute step size allowed. The default value is 0. 
hmax 
The maximum absolute step size allowed. The default checks for the maximum difference in times in your sampling and events, and uses this value. The value 0 is equivalent to infinite maximum absolute step size. 
hini 
The step size to be attempted on the first step. The default value is determined by the solver (when hini = 0) 
maxordn 
The maximum order to be allowed for the nonstiff (Adams) method. The default is 12. It can be between 1 and 12. 
maxords 
The maximum order to be allowed for the stiff (BDF) method. The default value is 5. This can be between 1 and 5. 
cores 
Number of cores used in parallel ODE solving. This
defaults to the number or system cores determined by

covsInterpolation 
specifies the interpolation method for
timevarying covariates. When solving ODEs it often samples
times outside the sampling time specified in

addCov 
A boolean indicating if covariates should be added to the output matrix or data frame. By default this is disabled. 
matrix 
A boolean inticating if a matrix should be returned instead of the RxODE's solved object. 
sigma 
Named sigma covariance or Cholesky decomposition of a covariance matrix. The names of the columns indicate parameters that are simulated. These are simulated for every observation in the solved system. 
sigmaDf 
Degrees of freedom of the sigma tdistribution. By
default it is equivalent to 
nCoresRV 
Number of cores used for the simulation of the
sigma variables. By default this is 1. This uses the package

sigmaIsChol 
Boolean indicating if the sigma is in the Cholesky decomposition instead of a symmetric covariance 
nDisplayProgress 
An integer indicating the minimum number of cbased solves before a progress bar is shown. By default this is 10,000. 
amountUnits 
This supplies the dose units of a data frame supplied instead of an event table. This is for importing the data as an RxODE event table. 
timeUnits 
This supplies the time units of a data frame supplied instead of an event table. This is for importing the data as an RxODE event table. 
stiff 
a logical ( For stiff ODE sytems ( For nonstiff systems ( 
theta 
A vector of parameters that will be named THETA[#] and added to parameters 
eta 
A vector of parameters that will be named ETA[#] and added to parameters 
addDosing 
Boolean indicating if the solve should add RxODE
evid and amt columns. This will also include dosing
information and estimates at the doses. Be default, RxODE
only includes estimates at the observations. (default

updateObject 
This is an internally used flag to update the
RxODE solved object (when supplying an RxODE solved object) as
well as returning a new object. You probably should not
modify it's 
doSolve 
Internal flag. By default this is 
omega 
Named omega matrix. 
omegaDf 
The degrees of freedom of a tdistribution for
simulation. By default this is 
omegaIsChol 
Indicates if the 
nSub 
Number between subject variabilities (ETAs) simulated for every realization of the parameters. 
thetaMat 
Named theta matrix. 
thetaDf 
The degrees of freedom of a tdistribution for
simulation. By default this is 
thetaIsChol 
Indicates if the 
nStud 
Number virtual studies to characterize uncertainty in estimated parameters. 
dfSub 
Degrees of freedom to sample the between subject variaiblity matrix from the inverse Wishart distribution (scaled) or scaled inverse chi squared distribution. 
dfObs 
Degrees of freedom to sample the unexplained variaiblity matrix from the inverse Wishart distribution (scaled) or scaled inverse chi squared distribution. 
returnType 
This tells what type of object is returned. The currently supported types are:

seed 
an object specifying if and how the random number generator should be initialized 
nsim 
represents the number of simulations. For RxODE, if you supply single subject event tables (created with eventTable) 
a 
when using 
b 
when using 
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