```
# NOT RUN {
str(lmerControl())
str(glmerControl())
## fit with default algorithm [nloptr version of BOBYQA] ...
fm0 <- lmer(Reaction ~ Days + ( 1 | Subject), sleepstudy)
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
## or with "bobyqa" (default 2013 - 2019-02) ...
fm1_bobyqa <- update(fm1, control = lmerControl(optimizer="bobyqa"))
## or with "Nelder_Mead" (the default till 2013) ...
fm1_NMead <- update(fm1, control = lmerControl(optimizer="Nelder_Mead"))
## or with the nlminb function used in older (<1.0) versions of lme4;
## this will usually replicate older results
if (require(optimx)) {
fm1_nlminb <- update(fm1,
control = lmerControl(optimizer= "optimx",
optCtrl = list(method="nlminb")))
## The other option here is method="L-BFGS-B".
}
## Or we can wrap base::optim():
optimwrap <- function(fn,par,lower,upper,control=list(),
...) {
if (is.null(control$method)) stop("must specify method in optCtrl")
method <- control$method
control$method <- NULL
## "Brent" requires finite upper values (lower bound will always
## be zero in this case)
if (method=="Brent") upper <- pmin(1e4,upper)
res <- optim(par=par, fn=fn, lower=lower,upper=upper,
control=control,method=method,...)
with(res, list(par = par,
fval = value,
feval= counts[1],
conv = convergence,
message = message))
}
fm0_brent <- update(fm0,
control = lmerControl(optimizer = "optimwrap",
optCtrl = list(method="Brent")))
## You can also use functions (in addition to the lmerControl() default "NLOPT_BOBYQA")
## from the 'nloptr' package, see also '?nloptwrap' :
if (require(nloptr)) {
fm1_nloptr_NM <- update(fm1, control=lmerControl(optimizer="nloptwrap",
optCtrl=list(algorithm="NLOPT_LN_NELDERMEAD")))
fm1_nloptr_COBYLA <- update(fm1, control=lmerControl(optimizer="nloptwrap",
optCtrl=list(algorithm="NLOPT_LN_COBYLA",
xtol_rel=1e-6,
xtol_abs=1e-10,
ftol_abs=1e-10)))
}
## other algorithm options include NLOPT_LN_SBPLX
# }
```

Run the code above in your browser using DataCamp Workspace