nlmixr2est (version 5.0.2)

agqControl: Control options for the agq 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

agqControl(
  sigdig = 3,
  nAGQ = 2,
  ...,
  interaction = TRUE,
  agqLow = -Inf,
  agqHi = Inf
)

Value

agqControl 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)

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.

...

Parameters used in the default `foceiControl()`

interaction

boolean, Interaction term for the model, in this case the default is `TRUE`; For adaptive quadrature, with normal distribution the Hessian is calculated with the foce(i) approximation

agqLow

The lower bound for adaptive quadrature log-likelihood. By default this is -Inf; in the original nlmixr's gnlmm it was -700.

agqHi

The upper bound for adaptive quadrature log-likelihood. By default this is Inf; in the original nlmixr's gnlmm was 400.

Author

Matthew L. Fidler

Examples

# \donttest{

agqControl()

# 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="agq")


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="agq", control=agqControl(nAGQ=5))

one.cmt <- function() {
  ini({
    ## You may label each parameter with a comment
    tka <- 0.45 # Log Ka
    tcl <- log(c(0, 2.7, 100)) # Log Cl
    ## This works with interactive models
    ## You may also label the preceding line with label("label text")
    tv <- 3.45; label("log V")
    ## the label("Label name") works with all models
    eta.ka ~ 0.6
    eta.cl ~ 0.3
    eta.v ~ 0.1
    add.sd <- 0.7
 })
 model({
   ka <- exp(tka + eta.ka)
   cl <- exp(tcl + eta.cl)
   v <- exp(tv + eta.v)
   linCmt() ~ add(add.sd)
 })
}

fit <- nlmixr(one.cmt, theo_sd, est="agq")

# }