lme4: nloptwrap – R documentation

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nloptwrap

Wrappers for additional optimizers

Description

Wrappers to allow use of alternative optimizers, from the NLopt library (via nloptr) or elsewhere, for the nonlinear optimization stage.

Usage

nloptwrap (par, fn, lower, upper, control = list(), ...)
nlminbwrap(par, fn, lower, upper, control = list(), ...)

Arguments

`par`	starting parameter vector
`fn`	objective function
`lower, upper`	numeric vector of lower and upper bounds.
`control`	`list` of control parameters, corresponding to `optCtrl = *`, e.g., in `lmerControl()`. For `nloptwrap`, see `defaultControl` in ‘Details’.
`...`	additional arguments to be passed to objective function

Details

Using alternative optimizers is an important trouble-shooting tool for mixed models. These wrappers provide convenient access to the optimizers provided by Steven Johnson's NLopt library (via the nloptr R package), and to the nlminb optimizer from base R. nlminb is also available via the optimx package; this wrapper provides access to nlminb() without the need to install/link the package, and without the additional post-fitting checks that are implemented by optimx (see examples below).

One important difference between the nloptr-provided implementation of BOBYQA and the minqa-provided version accessible via optimizer="bobyqa" is that it provides simpler access to optimization tolerances. bobyqa provides only the rhoend parameter (“[t]he smallest value of the trust region radius that is allowed”), while nloptr provides a more standard set of tolerances for relative or absolute change in the objective function or the parameter values (ftol_rel, ftol_abs, xtol_rel, xtol_abs).

The default (empty) control list corresponds to the following settings:

nlminbwrap:

control exactly corresponds to nlminb()'s defaults, see there.

nloptwrap:

environment(nloptwrap)$defaultControl contains the defaults, notably algorithm = "NLOPT_LN_BOBYQA".

nloptr::nloptr.print.options() shows and explains the many possible algorithm and options.

Value

`par`	estimated parameters
`fval`	objective function value at minimum
`feval`	number of function evaluations
`conv`	convergence code (0 if no error)
`message`	convergence message

Author(s)

Gabor Grothendieck (nlminbwrap)

Examples

library(lme4)
ls.str(environment(nloptwrap)) # 'defaultControl' algorithm "NLOPT_LN_BOBYQA"
## Note that  'optimizer =  "nloptwrap"' is now the default for lmer() :
fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
## tighten tolerances
fm1B <- update(fm1, control= lmerControl(optCtrl = list(xtol_abs=1e-8, ftol_abs=1e-8)))
## run for longer (no effect in this case)
fm1C <- update(fm1,control = lmerControl(optCtrl = list(maxeval=10000)))

  logLik(fm1B) - logLik(fm1)  ## small difference in log likelihood
c(logLik(fm1C) - logLik(fm1)) ## no difference in LL
## Nelder-Mead
fm1_nloptr_NM <- update(fm1, control=
                  lmerControl(optimizer = "nloptwrap",
                              optCtrl = list(algorithm = "NLOPT_LN_NELDERMEAD")))
## other nlOpt algorithm options include NLOPT_LN_COBYLA, NLOPT_LN_SBPLX, see
if(interactive())
  nloptr::nloptr.print.options()

fm1_nlminb <- update(fm1, control=lmerControl(optimizer = "nlminbwrap"))
if (require(optimx)) { ## the 'optimx'-based nlminb :
  fm1_nlminb2 <- update(fm1, control=
                lmerControl(optimizer = "optimx",
                            optCtrl = list(method="nlminb", kkt=FALSE)))
  cat("Likelihood difference (typically zero):  ",
      c(logLik(fm1_nlminb) - logLik(fm1_nlminb2)), "\n")
}

lme4

Linear Mixed-Effects Models using 'Eigen' and S4

v1.1-26

GPL (>= 2)

Authors

Douglas Bates [aut] (<https://orcid.org/0000-0001-8316-9503>), Martin Maechler [aut] (<https://orcid.org/0000-0002-8685-9910>), Ben Bolker [aut, cre] (<https://orcid.org/0000-0002-2127-0443>), Steven Walker [aut] (<https://orcid.org/0000-0002-4394-9078>), Rune Haubo Bojesen Christensen [ctb] (<https://orcid.org/0000-0002-4494-3399>), Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>), Bin Dai [ctb], Fabian Scheipl [ctb] (<https://orcid.org/0000-0001-8172-3603>), Gabor Grothendieck [ctb], Peter Green [ctb] (<https://orcid.org/0000-0002-0238-9852>), John Fox [ctb], Alexander Bauer [ctb], Pavel N. Krivitsky [ctb, cph] (<https://orcid.org/0000-0002-9101-3362>, shared copyright on simulate.formula)

Initial release