nlmixr2est (version 5.0.2)

laplaceControl: 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)

Value

laplaceControl object

Arguments

sigdig

Optimization significant digits. This controls:

• The tolerance of the inner and outer optimization is 10^-sigdig

• The tolerance of the ODE solvers is 0.5*10^(-sigdig-2); For the sensitivity equations and steady-state solutions the default is 0.5*10^(-sigdig-1.5) (sensitivity changes only applicable for liblsoda)

• The tolerance of the boundary check is 5 * 10 ^ (-sigdig + 1)

...

Parameters used in the default `foceiControl()`

nAGQ

Number of Gauss-Hermite Adaptive Quadrature points to take. When `nAGQ=0`, the AGQ is not used. With `nAGQ=1`, this is equivalent to the Laplace method. The adaptive quadrature expands every node for each of the ETAs, so it can be quite expensive with a large amount of ETAs. Once the EBE is obtained for a subject, you will have nAGQ^neta additional function evaluations for even nAGQ numbers and (nAGQ^neta)-1 additional function evaluations for odd nAGQ numbers.

Author

Matthew L. Fidler

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.

Examples

# \donttest{

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")

# }