This is for the so-called inner problem.
rxSEinner(
obj,
predfn,
pkpars = NULL,
errfn = NULL,
init = NULL,
grad = FALSE,
sum.prod = FALSE,
pred.minus.dv = TRUE,
only.numeric = FALSE,
optExpression = TRUE,
interaction = TRUE,
...,
promoteLinSens = TRUE,
theta = FALSE,
addProp = c("combined2", "combined1")
)rxSymPySetupPred(
obj,
predfn,
pkpars = NULL,
errfn = NULL,
init = NULL,
grad = FALSE,
sum.prod = FALSE,
pred.minus.dv = TRUE,
only.numeric = FALSE,
optExpression = TRUE,
interaction = TRUE,
...,
promoteLinSens = TRUE,
theta = FALSE,
addProp = c("combined2", "combined1")
)
RxODE object
Prediction function
Pk Pars function
Error function
Initialization parameters for scaling.
Boolaen indicated if the the equations for the gradient be calculated
A boolean determining if RxODE should use more numerically stable sums/products.
Boolean stating if the FOCEi objective function is based on PRED-DV (like NONMEM). Default TRUE.
Instead of setting up the sensitivities for the inner problem, modify the RxODE to use numeric differentiation for the numeric inner problem only.
Optimize the model text for computer evaluation.
Boolean to determine if dR^2/deta is calculated for FOCEi (not needed for FOCE)
Promote solved linear compartment systems to sensitivity-based solutions.
Calculate THETA derivatives instead of ETA derivatives. By default FALSE
one of "combined1" and "combined2"; These are the two forms of additive+proportional errors supported by monolix/nonmem:
combined1: transform(y)=transform(f)+(a+b*f^c)*eps
combined2: transform(y)=transform(f)+(a^2+b^2*f^(2c))*eps
RxODE object expanded with predfn and with calculated sensitivities.