Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
View source: R/lmer.R
This function overloads lmer
from the lme4package
(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.
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14  (
,
= ,
REML = ,
control = lmerControl(),
= ,
verbose = 0L,
,
,
,
,
= ,
devFunOnly =
)

formula 
a twosided linear formula object describing both the
fixedeffects and randomeffects part of the model, with the
response on the left of a ~ operator and the terms, separated
by + operators, on the right. Randomeffects 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 loglikelihood)?

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).

For details about lmer
see lmer
(help(lme4::lmer)
). The description of all arguments is taken
unedited from the lme4package.
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.
an S4 object of class "lmerModLmerTest"
Rune Haubo B. Christensen and Alexandra Kuznetsova for the overload
in lmerTest – lme4authors for the underlying implementation
in lme4.
lmer
and lmerModLmerTest
 ("sleepstudy", package="lme4")
m < (Reaction ~ Days + (Days  Subject), sleepstudy)
(m) # lmerModLmerTest

lmerTest documentation built on Oct. 23, 2020, 6:16 p.m.