Advance a linear compartment model by one step

Advances the amounts of a one, two or three compartment linear model by dt and returns the central concentration, optionally with its derivatives. This is the per-row kernel behind rxode2's linCmt().

Usage

linCmtModelDouble(
  dt,
  p1,
  v1,
  p2,
  p3,
  p4,
  p5,
  ka,
  alastNV,
  rateNV,
  ncmt,
  oral0,
  trans,
  deriv,
  type,
  tau,
  tinf,
  amt,
  bolusCmt,
  ndiff,
  sensType = 3L,
  sensH = 0.001
)

Arguments

dt

time step

p1, v1, p2, p3, p4, p5

parameters in the trans parameterization (see linCmtMicros())

ka

absorption rate constant (used when oral0 is 1)

alastNV

amounts at the start of the step (depot first when oral0 is 1, then central and peripherals); with deriv = TRUE followed by their parameter sensitivities

rateNV

zero-order rates into each compartment, in the same order

ncmt

number of compartments, 1 to 3

oral0

1 when the model has a depot compartment, otherwise 0

trans

parameterization number

deriv

logical; also return the derivatives

type

0 for a regular step; 1 and 2 steady-state infusion, 3 steady-state bolus

tau

steady-state dosing interval

tinf

steady-state infusion duration

amt

steady-state bolus amount

bolusCmt

steady-state bolus compartment

ndiff

differentiation bit mask as used by rxode2 (ka 1, p1 2, v1 4, p2 8, p3 16, p4 32, p5 64)

sensType

derivative method: 3 or 30 forward-mode automatic differentiation, 32 all directions in one forward-mode pass, 31 reverse mode, 100 automatic choice, 1 and 2 forward and central differences with the kernel's own step, 10 and 20 forward and central differences with step sensH

sensH

finite-difference step for sensType 10 and 20

Value

list with val (central concentration) and Alast (carried state after the step); with deriv = TRUE also J (Jacobian of the amounts) and Jg (gradient of val)

Examples

linCmtModelDouble(1, 2, 20, 0, 0, 0, 0, 0, 100, 0, 1L, 0L, 1L, FALSE,
                  0L, 0, 0, 0, 0L, 0L)
#> $val
#> [1] 4.524187
#> 
#> $Alast
#> [1] 90.48374
#>