Control parameters for glmmTMB optimization
glmmTMBControl(
optCtrl = NULL,
optArgs = list(),
optimizer = nlminb,
profile = FALSE,
collect = FALSE,
parallel = getOption("glmmTMB.cores", 1L),
eigval_check = TRUE,
zerodisp_val = log(.Machine$double.eps)/4,
start_method = list(method = NULL, jitter.sd = 0),
rank_check = c("adjust", "warning", "stop", "skip"),
conv_check = c("warning", "skip")
)Passed as argument control to optimizer. Default value (if default nlminb optimizer is used): list(iter.max=300, eval.max=400)
additional arguments to be passed to optimizer function (e.g.: list(method="BFGS") when optimizer=optim)
Function to use in model fitting. See Details for required properties of this function.
(logical) Experimental option to improve speed and robustness when a model has many fixed effects
(logical) Experimental option to improve speed by recognizing duplicated observations.
(integer) Set number of OpenMP threads to evaluate
the negative log-likelihood in parallel. The default is to evaluate
models serially (i.e. single-threaded); users can set a default value
for an R session via options(glmmTMB.cores=<value>). At present
reduced-rank models (i.e., a covariance structure using rr(...))
cannot be fitted in parallel; the number of threads will be automatically
set to 1, with a warning if this overrides the user-specified value.
Check eigenvalues of variance-covariance matrix? (This test may be very slow for models with large numbers of fixed-effect parameters.)
value of the dispersion parameter when dispformula=~0 is specified
(list) Options to initialize the starting values when fitting models with reduced-rank (rr) covariance structures; jitter.sd adds variation to the starting values of latent variables when method = "res".
Check whether all parameters in fixed-effects models are identifiable? This test may be slow for models with large numbers of fixed-effect parameters, therefore default value is 'warning'. Alternatives include 'skip' (no check), 'stop' (throw an error), and 'adjust' (drop redundant columns from the fixed-effect model matrix).
Do basic checks of convergence (check for non-positive definite Hessian and non-zero convergence code from optimizer). Default is 'warning'; 'skip' ignores these tests (not recommended for general use!)
By default, glmmTMB uses the nonlinear optimizer
nlminb for parameter estimation. Users may sometimes
need to adjust optimizer settings in order to get models to
converge. For instance, the warning ‘iteration limit reached
without convergence’ may be fixed by increasing the number of
iterations using (e.g.)
glmmTMBControl(optCtrl=list(iter.max=1e3,eval.max=1e3)).
Setting profile=TRUE allows glmmTMB to use some special
properties of the optimization problem in order to speed up estimation
in cases with many fixed effects.
Control parameters may depend on the model specification. The value
of the controls is evaluated inside an R object that is derived from
the output of the mkTMBStruc function. For example,
to specify that profile should be enabled if the model has
more than 5 fixed-effect parameters, specify
profile=quote(length(parameters$beta)>=5)
The optimizer argument can be any optimization (minimizing) function, provided that:
the first three arguments, in order, are the starting values, objective function, and gradient function;
the function also takes a control argument;
the function returns a list with elements (at least) par, objective, convergence (0 if convergence is successful) and message
(glmmTMB automatically handles output from optim(), by renaming the value component to objective)
## fit with default (nlminb) and alternative (optim/BFGS) optimizer
m1 <- glmmTMB(count~ mined, family=poisson, data=Salamanders)
m1B <- update(m1, control=glmmTMBControl(optimizer=optim,
optArgs=list(method="BFGS")))
## estimates are *nearly* identical:
all.equal(fixef(m1), fixef(m1B))
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