R: Setup Pred function based on RxODE object.

rxSEinner {RxODE} | R Documentation

## Setup Pred function based on RxODE object.

### Description

This is for the so-called inner problem.

### Usage

    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")
    )

### Arguments

`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

### Value

RxODE object expanded with predfn and with calculated sensitivities.

### Author(s)

Matthew L. Fidler

[Package RxODE version 1.1.2 Index]