saControl: Options for the SAEM stochastic-approximation covariance in...
In nlmixr2est: Nonlinear Mixed Effects Models in Population PK/PD, Estimation Routines

saControl

R Documentation

Options for the SAEM stochastic-approximation covariance in setCov()

Description

Used by setCov(fit, "sa"), which runs the SAEM covariance phase at the fit's estimates, after warm-up iterations unless it continues a SAEM fit's own chains. Every population parameter is held at the fit's estimates throughout (mixture proportions excepted), so the covariance is the one at those estimates.

Usage

saControl(
  nBurn = 100L,
  nEm = 100L,
  nSaCov = 500L,
  seed = 99L,
  warmStart = TRUE
)

Arguments

`nBurn`, `nEm`	warm-up iterations that equilibrate the MCMC chains before the covariance phase, for a fit without SAEM chain history (or with `warmStart = FALSE`)
`nSaCov`	iterations in the covariance phase; more gives a less noisy covariance
`seed`	random seed
`warmStart`	for a SAEM fit, continue the fit's own MCMC chains: the covariance phase starts from the fit's last iteration with no new warm-up iterations. This changes the Monte Carlo path, so the covariance differs from a cold start by Monte Carlo noise. Other fits, and SAEM fits that kept no chain history, always run `nBurn`/`nEm`.