Solve the full-trajectory adjoint sensitivities for a model

Reconstructs dy_k(t_i)/dp for every output state k, parameter p and output time t_i via backward (adjoint) integration, returning the same rx__sens_<state>_BY_<param>__ columns that forward sensitivities produce.

Usage

.rxAdjointSolve(
  object,
  params,
  events,
  calcSens,
  outTimes,
  adjStates = NULL,
  denseBy = 0.01,
  atol = 1e-10,
  rtol = 1e-10
)

Arguments

object

model definition accepted by rxode2() (text, rxode2 object, or function/ui).

params

named numeric vector of parameter values.

events

event table / data used to define dosing.

calcSens

character vector of parameter names to differentiate with respect to.

outTimes

numeric vector of output (observation) times at which the sensitivities are requested.

adjStates

character vector of output states of interest. Defaults to all ODE states (full forward-sensitivity parity).

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

data.frame with a time column and one rx__sens_<state>_BY_<param>__ column per (state, param) pair.

Author

Matthew L. Fidler