str(lmerControl())
str(glmerControl())
## fit with default Nelder-Mead algorithm ...
fm0 <- lmer(Reaction ~ Days + (1 | Subject), sleepstudy)
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
## or with minqa::bobyqa ...
fm1_bobyqa <- update(fm1,control=lmerControl(optimizer="bobyqa"))
## or with the nlminb function used in older (<1.0) versions of lme4;
## this will usually replicate older results
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 from the nloptr package.
if (require(nloptr)) {
defaultControl <- list(algorithm="NLOPT_LN_BOBYQA",
xtol_abs=1e-6,ftol_abs=1e-6,maxeval=1e5)
nloptwrap <- function(fn,par,lower,upper,control=list(),...) {
for (n in names(defaultControl))
if (is.null(control[[n]])) control[[n]] <- defaultControl[[n]]
res <- nloptr(x0=par,eval_f=fn,lb=lower,ub=upper,opts=control,...)
with(res,list(par=solution,
fval=objective,
feval=iterations,
conv=if (status>0) 0 else status,
message=message))
}
fm1_nloptr <- update(fm1,control=lmerControl(optimizer="nloptwrap"))
fm1_nloptr_NM <- update(fm1,control=lmerControl(optimizer="nloptwrap",
optCtrl=list(algorithm="NLOPT_LN_NELDERMEAD")))
}
## other algorithm options include NLOPT_LN_COBYLA, NLOPT_LN_SBPLXRun the code above in your browser using DataLab