This function overloads lmer
from the lme4-package
(lme4::lmer
) and adds a couple of slots needed for the computation of
Satterthwaite denominator degrees of freedom. All arguments are the same as
for lme4::lmer
and all the usual lmer
-methods work.
lmer(
formula,
data = NULL,
REML = TRUE,
control = lmerControl(),
start = NULL,
verbose = 0L,
subset,
weights,
na.action,
offset,
contrasts = NULL,
devFunOnly = FALSE
)
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.)
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).
logical scalar - Should the estimates be chosen to optimize the REML criterion (as opposed to the log-likelihood)?
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.
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"
.
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.
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.
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.
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.
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
.
an optional list. See the contrasts.arg
of
model.matrix.default
.
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).
an S4 object of class "lmerModLmerTest"
For details about lmer
see lmer
(help(lme4::lmer)
). The description of all arguments is taken
unedited from the lme4-package.
In cases when a valid lmer
-object
(lmerMod
) is produced, but when the computations needed for
Satterthwaite df fails, the lmerMod
object is returned - not an
lmerModLmerTest
object.
lmer
and lmerModLmerTest
# NOT RUN {
data("sleepstudy", package="lme4")
m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
class(m) # lmerModLmerTest
# }
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