# lmerControl

##### Control of Mixed Model Fitting

Construct control structures for mixed model fitting. All arguments have defaults, and can be grouped into

general control parameters, most importantly

`optimizer`

, further`restart_edge`

, etc;model- or data-checking specifications, in short “checking options”, such as

`check.nobs.vs.rankZ`

, or`check.rankX`

(currently not for`nlmerControl`

);all the parameters to be passed to the optimizer, e.g., maximal number of iterations, passed via the

`optCtrl`

list argument.

##### Usage

```
lmerControl(optimizer = "nloptwrap",
restart_edge = TRUE,
boundary.tol = 1e-5,
calc.derivs = TRUE,
use.last.params = FALSE,
sparseX = FALSE,
standardize.X = FALSE,
## input checking options
check.nobs.vs.rankZ = "ignore",
check.nobs.vs.nlev = "stop",
check.nlev.gtreq.5 = "ignore",
check.nlev.gtr.1 = "stop",
check.nobs.vs.nRE= "stop",
check.rankX = c("message+drop.cols", "silent.drop.cols", "warn+drop.cols",
"stop.deficient", "ignore"),
check.scaleX = c("warning","stop","silent.rescale",
"message+rescale","warn+rescale","ignore"),
check.formula.LHS = "stop",
## convergence checking options
check.conv.grad = .makeCC("warning", tol = 2e-3, relTol = NULL),
check.conv.singular = .makeCC(action = "message", tol = formals(isSingular)$tol),
check.conv.hess = .makeCC(action = "warning", tol = 1e-6),
## optimizer args
optCtrl = list(),
mod.type = "lmer"
)
```glmerControl(optimizer = c("bobyqa", "Nelder_Mead"),
restart_edge = FALSE,
boundary.tol = 1e-5,
calc.derivs = TRUE,
use.last.params = FALSE,
sparseX = FALSE,
standardize.X = FALSE,
## input checking options
check.nobs.vs.rankZ = "ignore",
check.nobs.vs.nlev = "stop",
check.nlev.gtreq.5 = "ignore",
check.nlev.gtr.1 = "stop",
check.nobs.vs.nRE= "stop",
check.rankX = c("message+drop.cols", "silent.drop.cols", "warn+drop.cols",
"stop.deficient", "ignore"),
check.scaleX = c("warning","stop","silent.rescale",
"message+rescale","warn+rescale","ignore"),
check.formula.LHS = "stop",
## convergence checking options
check.conv.grad = .makeCC("warning", tol = 2e-3, relTol = NULL),
check.conv.singular = .makeCC(action = "message", tol = formals(isSingular)$tol),
check.conv.hess = .makeCC(action = "warning", tol = 1e-6),
## optimizer args
optCtrl = list(),
mod.type = "glmer",
tolPwrss = 1e-7,
compDev = TRUE,
nAGQ0initStep = TRUE,
check.response.not.const = "stop"
)

nlmerControl(optimizer = "Nelder_Mead", tolPwrss = 1e-10,
optCtrl = list())

.makeCC(action, tol, relTol, …)

##### Arguments

- optimizer
character - name of optimizing function(s). A

`character`

vector or list of functions: length 1 for`lmer`

or`glmer`

, possibly length 2 for`glmer`

). Built-in optimizers are`"Nelder_Mead"`

,`"bobyqa"`

(from the minqa package),`"nlminbwrap"`

(using base R's`nlminb`

) and the default for`lmerControl()`

,`"nloptwrap"`

. Any minimizing function that allows box constraints can be used provided that it- (1)
takes input parameters

`fn`

(function to be optimized),`par`

(starting parameter values),`lower`

and`upper`

(parameter bounds) and`control`

(control parameters, passed through from the`control`

argument) and- (2)
returns a list with (at least) elements

`par`

(best-fit parameters),`fval`

(best-fit function value),`conv`

(convergence code, equal to zero for successful convergence) and (optionally)`message`

(informational message, or explanation of convergence failure).

Special provisions are made for

`bobyqa`

,`Nelder_Mead`

, and optimizers wrapped in the optimx package; to use the optimx optimizers (including`L-BFGS-B`

from base`optim`

and`nlminb`

), pass the`method`

argument to`optim`

in the`optCtrl`

argument (you may need to load the optimx package manually using`library(optimx)`

).For

`glmer`

, if`length(optimizer)==2`

, the first element will be used for the preliminary (random effects parameters only) optimization, while the second will be used for the final (random effects plus fixed effect parameters) phase. See`modular`

for more information on these two phases.If

`optimizer`

is`NULL`

(at present for`lmer`

only), all of the model structures will be set up, but no optimization will be done (e.g. parameters will all be returned as`NA`

).- calc.derivs
logical - compute gradient and Hessian of nonlinear optimization solution?

- use.last.params
logical - should the last value of the parameters evaluated (

`TRUE`

), rather than the value of the parameters corresponding to the minimum deviance, be returned? This is a "backward bug-compatibility" option; use`TRUE`

only when trying to match previous results.- sparseX
logical - should a sparse model matrix be used for the fixed-effects terms? Currently inactive.

- restart_edge
logical - should the optimizer attempt a restart when it finds a solution at the boundary (i.e. zero random-effect variances or perfect +/-1 correlations)? (Currently only implemented for

`lmerControl`

.)- boundary.tol
numeric - within what distance of a boundary should the boundary be checked for a better fit? (Set to zero to disable boundary checking.)

- tolPwrss
numeric scalar - the tolerance for declaring convergence in the penalized iteratively weighted residual sum-of-squares step.

- compDev
logical scalar - should compiled code be used for the deviance evaluation during the optimization of the parameter estimates?

- nAGQ0initStep
Run an initial optimization phase with

`nAGQ = 0`

. While the initial optimization usually provides a good starting point for subsequent fitting (thus increasing overall computational speed), setting this option to`FALSE`

can be useful in cases where the initial phase results in bad fixed-effect estimates (seen most often in binomial models with`link="cloglog"`

and offsets).- check.nlev.gtreq.5
character - rules for checking whether all random effects have >= 5 levels. See

`action`

.- check.nlev.gtr.1
character - rules for checking whether all random effects have > 1 level. See

`action`

.- check.nobs.vs.rankZ
character - rules for checking whether the number of observations is greater than (or greater than or equal to) the rank of the random effects design matrix (Z), usually necessary for identifiable variances. As for

`action`

, with the addition of`"warningSmall"`

and`"stopSmall"`

, which run the test only if the dimensions of`Z`

are < 1e6.`nobs > rank(Z)`

will be tested for LMMs and GLMMs with estimated scale parameters;`nobs >= rank(Z)`

will be tested for GLMMs with fixed scale parameter. The rank test is done using the`method="qr"`

option of the`rankMatrix`

function.- check.nobs.vs.nlev
character - rules for checking whether the number of observations is less than (or less than or equal to) the number of levels of every grouping factor, usually necessary for identifiable variances. As for

`action`

.`nobs<nlevels`

will be tested for LMMs and GLMMs with estimated scale parameters;`nobs<=nlevels`

will be tested for GLMMs with fixed scale parameter.- check.nobs.vs.nRE
character - rules for checking whether the number of observations is greater than (or greater than or equal to) the number of random-effects levels for each term, usually necessary for identifiable variances. As for

`check.nobs.vs.nlev`

.- check.conv.grad
rules for checking the gradient of the deviance function for convergence. A list as returned by

`.makeCC`

, or a character string with only the action.- check.conv.singular
rules for checking for a singular fit, i.e. one where some parameters are on the boundary of the feasible space (for example, random effects variances equal to 0 or correlations between random effects equal to +/- 1.0); as for

`check.conv.grad`

above. The default is to use`isSingular(.., tol = *)`

's default.- check.conv.hess
rules for checking the Hessian of the deviance function for convergence.; as for

`check.conv.grad`

above.- check.rankX
character - specifying if

`rankMatrix(X)`

should be compared with`ncol(X)`

and if columns from the design matrix should possibly be dropped to ensure that it has full rank. Sometimes needed to make the model identifiable. The options can be abbreviated; the three`"*.drop.cols"`

options all do drop columns,`"stop.deficient"`

gives an error when the rank is smaller than the number of columns where`"ignore"`

does no rank computation, and will typically lead to less easily understandable errors, later.- check.scaleX
character - check for problematic scaling of columns of fixed-effect model matrix, e.g. parameters measured on very different scales.

- check.formula.LHS
check whether specified formula has a left-hand side. Primarily for internal use within

`simulate.merMod`

;*use at your own risk*as it may allow the generation of unstable`merMod`

objects- check.response.not.const
character - check that the response is not constant.

- optCtrl
a

`list`

of additional arguments to be passed to the nonlinear optimizer (see`Nelder_Mead`

,`bobyqa`

). In particular, both`Nelder_Mead`

and`bobyqa`

use`maxfun`

to specify the maximum number of function evaluations they will try before giving up - in contrast to`optim`

and`optimx`

-wrapped optimizers, which use`maxit`

. (Also see`convergence`

for details of stopping tolerances for different optimizers.)*Note:*All of`lmer()`

,`glmer()`

and`nlmer()`

have an optional integer argument`verbose`

which you should raise (to a positive value) in order to get diagnostic console output about the optimization progress.- action
character - generic choices for the severity level of any test, with possible values

- "ignore":
skip the test.

- "warning":
warn if test fails.

- "message":
print a message if test fails.

- "stop":
throw an error if test fails.

- tol
(numeric) tolerance for checking the gradient, scaled relative to the curvature (i.e., testing the gradient on a scale defined by its Wald standard deviation)

- relTol
(numeric) tolerance for the gradient, scaled relative to the magnitude of the estimated coefficient

- mod.type
model type (for internal use)

- standardize.X
scale columns of X matrix? (not yet implemented)

- …
other elements to include in check specification

##### Details

Note that (only!) the pre-fitting “checking options”
(i.e., all those starting with `"check."`

but *not*
including the convergence checks (`"check.conv.*"`

) or
rank-checking (`"check.rank*"`

) options)
may also be set globally via `options`

.
In that case, `(g)lmerControl`

will use them rather than the
default values, but will *not* override values that are passed as
explicit arguments.

For example, `options(lmerControl=list(check.nobs.vs.rankZ = "ignore"))`

will suppress warnings that the number of observations is less than
the rank of the random effects model matrix `Z`

.

##### Value

The `*Control`

functions return a list (inheriting from class
`"merControl"`

) containing

general control parameters, such as

`optimizer`

,`restart_edge`

;(currently not for

`nlmerControl`

:)`"checkControl"`

, a`list`

of data-checking specifications, e.g.,`check.nobs.vs.rankZ`

;parameters to be passed to the optimizer, i.e., the

`optCtrl`

list, which may contain`maxiter`

.

`.makeCC`

returns a list containing the check specification
(action, tolerance, and optionally relative tolerance).

##### See Also

convergence and `allFit()`

which fits
for a couple of optimizers.

##### Examples

```
# 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
# }
```

*Documentation reproduced from package lme4, version 1.1-23, License: GPL (>= 2)*