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).

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. The default is to use isSingular(.., tol = *)'s default.

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. (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.

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.

(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)

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

model type (for internal use)

scale columns of X matrix? (not yet implemented)

other elements to include in check specification