- formula
a two-sided linear formula object describing both the
fixed-effects and random-effects part of the model, with the
response on the left of a `~`

operator and the terms, separated
by `+`

operators, on the right. Random-effects terms are
distinguished by vertical bars (`|`

) separating expressions
for design matrices from grouping factors. Two vertical bars
(`||`

) can be used to specify multiple uncorrelated random
effects for the same grouping variable.
(Because of the way it is implemented, the `||`

-syntax *works
only for design matrices containing numeric (continuous) predictors*;
to fit models with independent categorical effects, see `dummy`

or the `lmer_alt`

function from the afex package.)

- data
an optional data frame containing the variables named in
`formula`

. By default the variables are taken from the
environment from which `lmer`

is called. While `data`

is
optional, the package authors *strongly* recommend its use,
especially when later applying methods such as `update`

and
`drop1`

to the fitted model (*such methods are not
guaranteed to work properly if *`data`

is omitted). If
`data`

is omitted, variables will be taken from the environment
of `formula`

(if specified as a formula) or from the parent
frame (if specified as a character vector).

- REML
logical scalar - Should the estimates be chosen to
optimize the REML criterion (as opposed to the log-likelihood)?

- control
a list (of correct class, resulting from
`lmerControl()`

or `glmerControl()`

respectively) containing control parameters, including the nonlinear
optimizer to be used and parameters to be passed through to the
nonlinear optimizer, see the `*lmerControl`

documentation for
details.

- start
a named `list`

of starting values for the
parameters in the model. For `lmer`

this can be a numeric
vector or a list with one component named `"theta"`

.

- verbose
integer scalar. If `> 0`

verbose output is
generated during the optimization of the parameter estimates. If
`> 1`

verbose output is generated during the individual
penalized iteratively reweighted least squares (PIRLS) steps.

- subset
an optional expression indicating the subset of the rows
of `data`

that should be used in the fit. This can be a logical
vector, or a numeric vector indicating which observation numbers are
to be included, or a character vector of the row names to be
included. All observations are included by default.

- weights
an optional vector of ‘prior weights’ to be used
in the fitting process. Should be `NULL`

or a numeric vector.
Prior `weights`

are *not* normalized or standardized in
any way. In particular, the diagonal of the residual covariance
matrix is the squared residual standard deviation parameter
`sigma`

times the vector of inverse `weights`

.
Therefore, if the `weights`

have relatively large magnitudes,
then in order to compensate, the `sigma`

parameter will
also need to have a relatively large magnitude.

- na.action
a function that indicates what should happen when the
data contain `NA`

s. The default action (`na.omit`

,
inherited from the 'factory fresh' value of
`getOption("na.action")`

) strips any observations with any
missing values in any variables.

- offset
this can be used to specify an *a priori* known
component to be included in the linear predictor during
fitting. This should be `NULL`

or a numeric vector of length
equal to the number of cases. One or more `offset`

terms can be included in the formula instead or as well, and if more
than one is specified their sum is used. See
`model.offset`

.

- contrasts
an optional list. See the `contrasts.arg`

of
`model.matrix.default`

.

- devFunOnly
logical - return only the deviance evaluation
function. Note that because the deviance function operates on
variables stored in its environment, it may not return
*exactly* the same values on subsequent calls (but the results
should always be within machine tolerance).