lme4: allFit – R documentation

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allFit

Refit a fitted model with all available optimizers

Description

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 meth.tab)

Usage

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

Arguments

`object`	a fitted model
`meth.tab`	a matrix (or data.frame) with columns method the name of a specific optimization method to pass to the optimizer (leave blank for built-in optimizers) optimizer the `optimizer` function to use
`data`	data to be included with result (for later debugging etc.)
`verbose`	logical: report progress in detail?
`show.meth.tab`	logical: return table of methods?
`maxfun`	passed as part of `optCtrl` (see `lmeControl`)
`parallel`	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"`).
`ncpus`	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.
`cl`	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.

Details

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.

Value

an object of type allFit, which is a list of fitted merMod objects (unless show.meth.tab 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).

Examples

if (interactive()) {
library(lme4)
  gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
             data = cbpp, family = binomial)
  ## show available methods
  allFit(show.meth.tab=TRUE) 
  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")
  clusterEvalQ(optCls,library("lme4"))
  ### 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))
  stopCluster(optCls)

## End(Not run)

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