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.
lmerControl(optimizer = "bobyqa",
restart_edge = TRUE,
boundary.tol = 1e-5,
calc.derivs=TRUE,
use.last.params=FALSE,
sparseX = 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 = "ignore", tol = 1e-4),
check.conv.hess = .makeCC(action = "warning", tol = 1e-6),
## optimizer args
optCtrl = list())glmerControl(optimizer = c("bobyqa", "Nelder_Mead"),
restart_edge = FALSE,
boundary.tol = 1e-5,
calc.derivs=TRUE,
use.last.params=FALSE,
sparseX = FALSE,
tolPwrss=1e-7,
compDev=TRUE,
nAGQ0initStep=TRUE,
## 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 = "warning",
check.formula.LHS = "stop",
check.response.not.const = "stop",
## convergence checking options
check.conv.grad = .makeCC("warning", tol = 1e-3, relTol = NULL),
check.conv.singular = .makeCC(action = "ignore", tol = 1e-4),
check.conv.hess = .makeCC(action = "warning", tol = 1e-6),
## optimizer args
optCtrl = list())
nlmerControl(optimizer = "Nelder_Mead", tolPwrss = 1e-10,
optCtrl = list())
.makeCC(action, tol, relTol, …)
character - name of optimizing
function(s). A character vector or list of functions: length 1 for
lmer or glmer, possibly length 2 for glmer).
The built-in optimizers are Nelder_Mead and
bobyqa (from the minqa package). Any
minimizing function that allows box constraints can be used provided
that it
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
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 also
need to load the optimx package manually using
library(optimx) or require(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).
logical - compute gradient and Hessian of nonlinear optimization solution?
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.
logical - should a sparse model matrix be used for the fixed-effects terms? Currently inactive.
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.)
numeric - within what distance of a boundary should the boundary be checked for a better fit? (Set to zero to disable boundary checking.)
numeric scalar - the tolerance for declaring convergence in the penalized iteratively weighted residual sum-of-squares step.
logical scalar - should compiled code be used for the deviance evaluation during the optimization of the parameter estimates?
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).
character - rules for
checking whether all random effects have >= 5 levels.
See action.
character - rules for checking
whether all random effects have > 1 level. See action.
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.
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.
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.
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.
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.
rules for checking the Hessian of the deviance
function for convergence.; as for check.conv.grad
above.
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.
character - check for problematic scaling of columns of fixed-effect model matrix, e.g. parameters measured on very different scales.
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
character - check that the response is not constant.
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.
character - generic choices for the severity level of any test. "ignore": skip the test. "warning": warn if test fails. "stop": throw an error if test fails.
numeric - tolerance for check
numeric - tolerance for checking relative variation
other elements to include in check specification
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).
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.
# NOT RUN {
str(lmerControl())
str(glmerControl())
# }
# NOT RUN {
## fit with default "bobyqa" algorithm ...
fm0 <- lmer(Reaction ~ Days + ( 1 | Subject), sleepstudy)
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
## or with "Nelder_Mead" (the previous default) ...
fm1_bobyqa <- 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
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_SBPLX
# }
Run the code above in your browser using DataLab