# nloptwrap

0th

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Wrappers to allow use of alternative optimizers, from NLopt library or elsewhere, for 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
vector of lower bounds
upper
vector of upper bounds
control
list of control parameters
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. minqa::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).

##### 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

• nloptwrap
• nlminbwrap
##### Examples
environment(nloptwrap)\$defaultControl
library(lme4)
fm1 <- lmer(Reaction~Days+(Days|Subject),sleepstudy)
## BOBYQA (default)
fm1_nloptr <- update(fm1,control=lmerControl(optimizer="nloptwrap"))
fm1_nloptr_NM <- update(fm1,control=lmerControl(optimizer="nloptwrap",