Skip to contents

Evaluate a prebuilt adjoint objective gradient in C++

Source: R/adjoint.R
dot-rxAdjointGradEvalC.Rd

Covers continuous ODE, modeled bioavailability (F), modeled lag (alag), replace/multiply events, and modeled-rate/duration infusions. A single forward solve emits the Jacobian, forcing and dose-dual quantities on a fine grid; the backward costate + quadrature sweep, event jumps and gradient accumulation run in C++ (rxode2AdjointSweep). Numerically equivalent to .rxAdjointGradEval().

Usage

.rxAdjointGradEvalC(
  build,
  params,
  obsTimes,
  obs,
  weight = 1,
  denseBy = 0.01,
  atol = 1e-10,
  rtol = 1e-10
)

Arguments

build

object returned by .rxAdjointGradBuild().

params

named numeric vector of parameter values.

obsTimes

numeric observation times.

obs

numeric observed values aligned with obsTimes.

weight

numeric scalar or vector of least-squares observation weights (ignored when errModel is supplied).

denseBy

grid spacing for the forward checkpoint trajectory; smaller values reduce the covariate-interpolation error.

atol, rtol

solver tolerances used for both the forward checkpoint and the backward sweeps.

Value

named numeric vector dG/dtheta over the build's calcSens.

Author

Matthew L. Fidler