Setup Pred function based on RxODE object.
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")
)obj |
RxODE object |
predfn |
Prediction function |
pkpars |
Pk Pars function |
errfn |
Error function |
init |
Initialization parameters for scaling. |
grad |
Boolaen indicated if the the equations for the gradient be calculated |
sum.prod |
A boolean determining if RxODE should use more numerically stable sums/products. |
pred.minus.dv |
Boolean stating if the FOCEi objective function is based on PRED-DV (like NONMEM). Default TRUE. |
only.numeric |
Instead of setting up the sensitivities for the inner problem, modify the RxODE to use numeric differentiation for the numeric inner problem only. |
optExpression |
Optimize the model text for computer evaluation. |
interaction |
Boolean to determine if dR^2/deta is calculated for FOCEi (not needed for FOCE) |
promoteLinSens |
Promote solved linear compartment systems to sensitivity-based solutions. |
theta |
Calculate THETA derivatives instead of ETA derivatives. By default FALSE |
addProp |
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.
Matthew L. Fidler
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