allFit: Refit a fitted model with all available optimizers

View source: R/allFit.R

allFitR Documentation

Refit a fitted model with all available optimizers


Attempt to re-fit a [g]lmer model with a range of optimizers. The default is to use all known optimizers for R that satisfy the requirements (i.e. they do not require functions and allow box constraints: see ‘optimizer’ in lmerControl). These optimizers fall in four categories; (i) built-in (minqa::bobyqa, lme4::Nelder_Mead, nlminbwrap), (ii) wrapped via optimx (most of optimx's optimizers that allow box constraints require an explicit gradient function to be specified; the two provided here are the base R functions that can be accessed via optimx), (iii) wrapped via nloptr (see examples for the list of options), (iv) ‘dfoptim::nmkb’ (via the (unexported) nmkbw wrapper: this appears as ‘nmkbw’ in


allFit(object, = NULL, data=NULL,
       verbose = TRUE, = FALSE,
       maxfun = 1e5,
       parallel = c("no", "multicore", "snow"),
       ncpus = getOption("allFit.ncpus", 1L), cl = NULL,
       catch.errs = TRUE)



a fitted model

a matrix (or data.frame) with columns


the name of a specific optimization method to pass to the optimizer (leave blank for built-in optimizers)


the optimizer function to use


data to be included with result (for later debugging etc.)


logical: report progress in detail?

logical: return table of methods?


passed as part of optCtrl to set the maximum number of function evaluations: this is automatically converted to the correct specification (e.g. maxfun, maxfeval, maxit, etc.) for each optimizer


The type of parallel operation to be used (if any). If missing, the default is taken from the option "boot.parallel" (and if that is not set, "no").


integer: number of processes to be used in parallel operation: typically one would choose this to be the number of available CPUs. Use options(allFit.ncpus=X) to set the default value to X for the duration of an R session.


An optional parallel or snow cluster for use if parallel = "snow". If not supplied, a cluster on the local machine is created for the duration of the boot call.


(logical) Wrap model fits in tryCatch clause to skip over errors? (catch.errs=FALSE is probably only useful for debugging)


  • Needs packages optimx, and dfoptim to use all optimizers

  • If you are using parallel="snow" (e.g. when running in parallel on Windows), you will need to set up a cluster yourself and run clusterEvalQ(cl,library("lme4")) before calling allFit to make sure that the lme4 package is loaded on all of the workers

  • Control arguments in control$optCtrl that are unused by a particular optimizer will be silently ignored (in particular, the maxfun specification is only respected by bobyqa, Nelder_Mead, and nmkbw)

  • Because allFit works by calling update, it may be fragile if the original model call contains references to variables, especially if they were originally defined in other environments or no longer exist when allFit is called.


an object of type allFit, which is a list of fitted merMod objects (unless is specified, in which case a data frame of methods is returned). The summary method for this class extracts tables with a variety of useful information about the different fits (see examples).

See Also

slice,slice2D from the bbmle package


if (interactive()) {
  gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
             data = cbpp, family = binomial)
  ## show available methods
  gm_all <- allFit(gm1)
  ss <- summary(gm_all)
  ss$which.OK            ## logical vector: which optimizers worked?
  ## the other components only contain values for the optimizers that worked
  ss$llik                ## vector of log-likelihoods
  ss$fixef               ## table of fixed effects
  ss$sdcor               ## table of random effect SDs and correlations
  ss$theta               ## table of random effects parameters, Cholesky scale
## Not run: 
  ## Parallel examples for Windows
  nc <- detectCores()-1
  optCls <- makeCluster(nc, type = "SOCK")
  ### not necessary here because using a built-in
  ## data set, but in general you should clusterExport() your data
  clusterExport(optCls, "cbpp")
  system.time(af1 <- allFit(m0, parallel = 'snow', 
                          ncpus = nc, cl=optCls))

## End(Not run) 

lme4 documentation built on July 8, 2022, 9:05 a.m.