Simulate Parameters from a Theta/Omega specification
rxSimThetaOmega(
params = NULL,
omega = NULL,
omegaDf = NULL,
omegaLower = as.numeric(c(R_NegInf)),
omegaUpper = as.numeric(c(R_PosInf)),
omegaIsChol = FALSE,
omegaSeparation = "auto",
omegaXform = 1L,
nSub = 1L,
thetaMat = NULL,
thetaLower = as.numeric(c(R_NegInf)),
thetaUpper = as.numeric(c(R_PosInf)),
thetaDf = NULL,
thetaIsChol = FALSE,
nStud = 1L,
sigma = NULL,
sigmaLower = as.numeric(c(R_NegInf)),
sigmaUpper = as.numeric(c(R_PosInf)),
sigmaDf = NULL,
sigmaIsChol = FALSE,
sigmaSeparation = "auto",
sigmaXform = 1L,
nCoresRV = 1L,
nObs = 1L,
dfSub = 0,
dfObs = 0,
simSubjects = TRUE,
simVariability = as.logical(c(NA_LOGICAL))
)Named Vector of rxode2 model parameters
Estimate of Covariance matrix. When omega is a list,
assume it is a block matrix and convert it to a full matrix for
simulations. When omega is NA and you are using it with a
rxode2 ui model, the between subject variability described by
the omega matrix are set to zero.
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.
Lower bounds for simulated ETAs (by default -Inf)
Upper bounds for simulated ETAs (by default Inf)
Indicates if the omega supplied is a
Cholesky decomposed matrix instead of the traditional
symmetric matrix.
Omega separation strategy
Tells the type of separation strategy when
simulating covariance with parameter uncertainty with standard
deviations modeled in the thetaMat matrix.
"lkj" simulates the correlation matrix from the
rLKJ1 matrix with the distribution parameter eta
equal to the degrees of freedom nu by (nu-1)/2
"separation" simulates from the identity inverse Wishart
covariance matrix with nu degrees of freedom. This is then
converted to a covariance matrix and augmented with the modeled
standard deviations. While computationally more complex than the
"lkj" prior, it performs better when the covariance matrix
size is greater or equal to 10
"auto" chooses "lkj" when the dimension of the
matrix is less than 10 and "separation" when greater
than equal to 10.
When taking omega values from the thetaMat
simulations (using the separation strategy for covariance
simulation), how should the thetaMat values be turned int
standard deviation values:
identity This is when standard deviation values are
directly modeled by the params and thetaMat matrix
variance This is when the params and thetaMat
simulates the variance that are directly modeled by the
thetaMat matrix
log This is when the params and thetaMat
simulates log(sd)
nlmixrSqrt This is when the params and
thetaMat simulates the inverse cholesky decomposed matrix
with the x^2 modeled along the diagonal. This only works
with a diagonal matrix.
nlmixrLog This is when the params and
thetaMat simulates the inverse cholesky decomposed matrix
with the exp(x^2) along the diagonal. This only works
with a diagonal matrix.
nlmixrIdentity This is when the params and
thetaMat simulates the inverse cholesky decomposed matrix.
This only works with a diagonal matrix.
Number between subject variabilities (ETAs) simulated for every
realization of the parameters.
Named theta matrix.
Lower bounds for simulated population parameter
variability (by default -Inf)
Upper bounds for simulated population unexplained
variability (by default Inf)
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.
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. When sigma is NA and you are using it with a
rxode2 ui model, the unexplained variability described by
the sigma matrix are set to zero.
Lower bounds for simulated unexplained variability (by default -Inf)
Upper bounds for simulated unexplained variability (by default Inf)
Degrees of freedom of the sigma t-distribution. By
default it is equivalent to Inf, or a normal distribution.
Boolean indicating if the sigma is in the Cholesky decomposition instead of a symmetric covariance
separation strategy for sigma;
Tells the type of separation strategy when
simulating covariance with parameter uncertainty with standard
deviations modeled in the thetaMat matrix.
"lkj" simulates the correlation matrix from the
rLKJ1 matrix with the distribution parameter eta
equal to the degrees of freedom nu by (nu-1)/2
"separation" simulates from the identity inverse Wishart
covariance matrix with nu degrees of freedom. This is then
converted to a covariance matrix and augmented with the modeled
standard deviations. While computationally more complex than the
"lkj" prior, it performs better when the covariance matrix
size is greater or equal to 10
"auto" chooses "lkj" when the dimension of the
matrix is less than 10 and "separation" when greater
than equal to 10.
When taking sigma values from the thetaMat
simulations (using the separation strategy for covariance
simulation), how should the thetaMat values be turned int
standard deviation values:
identity This is when standard deviation values are
directly modeled by the params and thetaMat matrix
variance This is when the params and thetaMat
simulates the variance that are directly modeled by the
thetaMat matrix
log This is when the params and thetaMat
simulates log(sd)
nlmixrSqrt This is when the params and
thetaMat simulates the inverse cholesky decomposed matrix
with the x^2 modeled along the diagonal. This only works
with a diagonal matrix.
nlmixrLog This is when the params and
thetaMat simulates the inverse cholesky decomposed matrix
with the exp(x^2) along the diagonal. This only works
with a diagonal matrix.
nlmixrIdentity This is when the params and
thetaMat simulates the inverse cholesky decomposed matrix.
This only works with a diagonal matrix.
Number of cores used for the simulation of the sigma variables. By default this is 1. 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.
Number of observations to simulate (with sigma matrix)
Degrees of freedom to sample the between subject variability matrix from the inverse Wishart distribution (scaled) or scaled inverse chi squared distribution.
Degrees of freedom to sample the unexplained variability matrix from the inverse Wishart distribution (scaled) or scaled inverse chi squared distribution.
boolean indicated rxode2 should simulate subjects in studies (TRUE,
default) or studies (FALSE)
determines if the variability is simulated.
When NA (default) this is determined by the solver.
a data frame with the simulated subjects