laplaceControl: Control options for the Laplace estimation method

In nlmixr2est: Nonlinear Mixed Effects Models in Population PK/PD, Estimation Routines

Control options for the Laplace estimation method

Description

This is the control options for the adaptive Gauss-Hermite quadrature for the likelihood. Note that nAGQ=1 is the same as the Laplace method.

Usage

laplaceControl(sigdig = 3, ..., nAGQ = 1)

Arguments

sigdig	Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 3 this is atol = 1e-6, rtol = 1e-3.
...	Parameters used in the default 'foceiControl()'
nAGQ	Number of Gauss-Hermite adaptive quadrature points. '0' disables AGQ; '1' is equivalent to Laplace. Cost grows quickly with ETAs: once the EBE is found, expect 'nAGQ^neta' (even 'nAGQ') or '(nAGQ^neta)-1' (odd 'nAGQ') additional evaluations per subject.

Details

This method can be made to more closely matches NONMEM-style Laplace estimation by requesting the log-likelihood from STAN as well as numerically calculated Hessian matrix. This is done with adding '+dnorm()' to the model for any normal end-points.

Value

laplaceControl object

Author(s)

Matthew L. Fidler

Examples

laplaceControl()

# Use adaptive quadrature

# x =  Litter size after 21 days, and the modeled value

r <- rats
r$dv <- r$x

# Time is not used in this model, but it is required in nlmixr2
# currently, add a dummy value

r$time <- 0

f <- function() {
  ini({
    t1 <- 1
    t2 <- 1
    t3 <- 1
    eta1 ~ 1
  })
  model({
    lp <- t1 * x1 + t2 * x2 + (x1 + x2*t3) * eta1
    p <- pnorm(lp)
    m1 <- m # need to add outside of model specification
    x ~ dbinom(m1, p)
  })
}

fit <- nlmixr(f, r, est="laplace")


p <- pump

p$dv <- p$y
p$time <- 0 # dummy time

f <- function() {
  ini({
    t1 <- 1
    t2 <- 1
    t3 <- 1
    t4 <- 1
    eta1 ~ 1
  })
  model({
    if (group == 1) {
       lp <- t1 + t2 * logtstd
    } else {
       lp <- t3 + t4 * logtstd
    }
    lp <- lp + eta1
    lam <- exp(lp)
    y ~ dpois(lam)
  })
}

fit <- nlmixr(f, p, est="laplace")

nlmixr2est documentation built on Aug. 5, 2026, 1:11 a.m.