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.
nlmixrSim(object, ...)# S3 method for nlmixrFitData
rxSolve(object, params = NULL, events = NULL,
inits = NULL, scale = NULL, method = c("liblsoda", "lsoda",
"dop853"), transitAbs = NULL, atol = 1e-06, rtol = 1e-04,
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, 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 nlmixrFitData
simulate(object, nsim = 1, seed = NULL, ...)
# S3 method for nlmixrFitData
solve(a, b, ...)
nlmixr object
Other arguments sent to rxSolve
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;
an eventTable object describing the input
(e.g., doses) to the dynamic system and observation sampling
time points (see eventTable);
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);
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 scale=c(center=2) will divide the center ODE
variable by 2.
The method for solving ODEs. Currently this supports:
"liblsoda" thread safe lsoda. This supports parallel
thread-based solving, and ignores user Jacobian specification.
"lsoda" -- LSODA solver. Does not support parallel thread-based
solving, but allows user Jacobian specification.
"dop853" -- DOP853 solver. Does not support parallel thread-based
solving nor user Jacobain specification
boolean indicating if this is a transit compartment absorption
a numeric absolute tolerance (1e-8 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.
a numeric relative tolerance (1e-6 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.
maximum number of (internally defined) steps allowed during one call to the solver. (5000 by default)
The minimum absolute step size allowed. The default value is 0.
The maximum absolute step size allowed. When
hmax=NA (default), uses the average difference (+hmaxSd*sd) in times
and sampling events. When hmax=NULL RxODE uses the
maximum difference in times in your sampling and events. The
value 0 is equivalent to infinite maximum absolute step size.
The step size to be attempted on the first step. The default value is determined by the solver (when hini = 0)
The maximum order to be allowed for the nonstiff (Adams) method. The default is 12. It can be between 1 and 12.
The maximum order to be allowed for the stiff (BDF) method. The default value is 5. This can be between 1 and 5.
Number of cores used in parallel ODE solving. This
defaults to the number or system cores determined by
rxCores for methods that support parallel
solving (ie thread-safe methods like "liblsoda").
specifies the interpolation method for
time-varying covariates. When solving ODEs it often samples
times outside the sampling time specified in events.
When this happens, the time varying covariates are
interpolated. Currently this can be:
"linear" interpolation (the default), which interpolates the covariate
by solving the line between the observed covariates and extrapolating the new
covariate value.
"constant" -- Last observation carried forward.
"NOCB" -- Next Observation Carried Backward. This is the same method
that NONMEM uses.
"midpoint" Last observation carried forward to midpoint; Next observation
carried backward to midpoint.
A boolean indicating if covariates should be added to the output matrix or data frame. By default this is disabled.
A boolean inticating if a matrix should be returned instead of the RxODE's solved object.
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.
Degrees of freedom of the sigma t-distribution. By
default it is equivalent to Inf, or a normal distribution.
Number of cores used for the simulation of the
sigma variables. By default this is 1. This uses the package
rmvn and rmvt.
To reproduce the results you need to run on the same platform
with the same number of cores. This is the reason this is set
to be one, regardless of what the number of cores are used in
threaded ODE solving.
Boolean indicating if the sigma is in the Cholesky decomposition instead of a symmetric covariance
An integer indicating the minimum number of c-based solves before a progress bar is shown. By default this is 10,000.
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.
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.
a logical (TRUE by default) indicating whether
the ODE system is stiff or not.
For stiff ODE sytems (stiff = TRUE), RxODE uses the
LSODA (Livermore Solver for Ordinary Differential Equations)
Fortran package, which implements an automatic method switching
for stiff and non-stiff problems along the integration
interval, authored by Hindmarsh and Petzold (2003).
For non-stiff systems (stiff = FALSE), RxODE uses
DOP853, an explicit Runge-Kutta method of order 8(5, 3) of
Dormand and Prince as implemented in C by Hairer and Wanner
(1993).
A vector of parameters that will be named THETA[#] and added to parameters
A vector of parameters that will be named ETA[#] and added to parameters
Boolean indicating if the solve should add RxODE
EVID and related columns. This will also include dosing
information and estimates at the doses. Be default, RxODE
only includes estimates at the observations. (default
FALSE). When addDosing is NULL, only
include EVID=0 on solve and exclude any model-times or
EVID=2. If addDosing is NA the classic
RxODE EVID events. When addDosing is TRUE
add the event information in NONMEM-style format; If
subsetNonmem=FALSE RxODE will also extra event types
(EVID) for ending infusion and modeled times:
EVID=-1 when the modeled rate infusions are turned
off (matches rate=-1)
EVID=-2 When the modeled duration infusions are
turned off (matches rate=-2)
EVID=-10 When the specified rate infusions are
turned off (matches rate>0)
EVID=-20 When the specified dur infusions are
turned off (matches dur>0)
EVID=101,102,103,... Modeled time where 101 is the
first model time, 102 is the second etc.
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 FALSE default unless you are willing to
have unexpected results.
Estimate of Covariance matrix. When omega is a list, assume it is a block matrix and convert it to a full matrix for simulations.
The degrees of freedom of a t-distribution for
simulation. By default this is NULL which is
equivalent to Inf degrees, or to simulate from a normal
distribution instead of a t-distribution.
Indicates if the omega supplied is a
Cholesky decomposed matrix instead of the traditional
symmetric matrix.
Number between subject variabilities (ETAs) simulated for every realization of the parameters.
Named theta matrix.
The degrees of freedom of a t-distribution for
simulation. By default this is NULL which is
equivalent to Inf degrees, or to simulate from a normal
distribution instead of a t-distribution.
Indicates if the theta supplied is a
Cholesky decomposed matrix instead of the traditional
symmetric matrix.
Number virtual studies to characterize uncertainty in estimated parameters.
Degrees of freedom to sample the between subject variaiblity matrix from the inverse Wishart distribution (scaled) or scaled inverse chi squared distribution.
Degrees of freedom to sample the unexplained variaiblity matrix from the inverse Wishart distribution (scaled) or scaled inverse chi squared distribution.
This tells what type of object is returned. The currently supported types are:
"rxSolve" (default) will return a reactive data frame
that can change easily change different pieces of the solve and
update the data frame. This is the currently standard solving
method in RxODE, is used for rxSolve(object, ...), solve(object,...),
"data.frame" -- returns a plain, non-reactive data
frame; Currently very slightly Faster than returnType="matrix"
"matrix" -- returns a plain matrix with column names attached
to the solved object. This is what is used object$run as well as object$solve
an object specifying if and how the random number generator should be initialized
represents the number of simulations. For RxODE, if you supply single subject event tables (created with eventTable)
when using solve, this is equivalent to the
object argument. If you specify object later in
the argument list it overwrites this parameter.
when using solve, this is equivalent to the
params argument. If you specify params as a
named argument, this overwrites the output