lmer {lme4}  R Documentation 
Fit a linear mixedeffects model (LMM) to data, via REML or maximum likelihood.
lmer(formula, data = NULL, REML = TRUE, control = lmerControl(),
start = NULL, verbose = 0L, subset, weights, na.action,
offset, contrasts = NULL, devFunOnly = FALSE)
formula 
a twosided linear formula object describing both the
fixedeffects and randomeffects part of the model, with the
response on the left of a 
data 
an optional data frame containing the variables named in

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

start 
a named 
verbose 
integer scalar. If 
subset 
an optional expression indicating the subset of the rows
of 
weights 
an optional vector of ‘prior weights’ to be used
in the fitting process. Should be 
na.action 
a function that indicates what should happen when the
data contain 
offset 
this can be used to specify an a priori known
component to be included in the linear predictor during
fitting. This should be 
contrasts 
an optional list. See the 
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). 
If the formula
argument is specified as a character
vector, the function will attempt to coerce it to a formula.
However, this is not recommended (users who want to construct
formulas by pasting together components are advised to use
as.formula
or reformulate
); model fits
will work but subsequent methods such as drop1
,
update
may fail.
When handling perfectly collinear predictor variables
(i.e. design matrices of less than full rank),
[gn]lmer
is not quite as sophisticated
as some simpler modeling frameworks such as
lm
and glm
. While it does
automatically drop collinear variables (with a message
rather than a warning), it does not automatically fill
in NA
values for the dropped coefficients;
these can be added via
fixef(fitted.model,add.dropped=TRUE)
.
This information can also be retrieved via
attr(getME(fitted.model,"X"),"col.dropped")
.
the deviance function returned when devFunOnly
is
TRUE
takes a single numeric vector argument, representing
the theta
vector. This vector defines the scaled
variancecovariance matrices of the random effects, in the
Cholesky parameterization. For models with only simple
(interceptonly) random effects, theta
is a vector of the
standard deviations of the random effects. For more complex or
multiple random effects, running getME(.,"theta")
to
retrieve the theta
vector for a fitted model and examining
the names of the vector is probably the easiest way to determine
the correspondence between the elements of the theta
vector
and elements of the lower triangles of the Cholesky factors of the
random effects.
An object of class merMod
(more specifically,
an object of subclass lmerMod
), for which many methods
are available (e.g. methods(class="merMod")
)
In earlier version of the lme4 package, a method
argument was
used. Its functionality has been replaced by the REML
argument.
Also, lmer(.)
allowed a family
argument (to effectively
switch to glmer(.)
). This has been deprecated in summer 2013,
and been disabled in spring 2019.
lm
for linear models;
glmer
for generalized linear; and
nlmer
for nonlinear mixed models.
## linear mixed models  reference values from older code
(fm1 < lmer(Reaction ~ Days + (Days  Subject), sleepstudy))
summary(fm1)# (with its own print method; see class?merMod % ./merModclass.Rd
str(terms(fm1))
stopifnot(identical(terms(fm1, fixed.only=FALSE),
terms(model.frame(fm1))))
attr(terms(fm1, FALSE), "dataClasses") # fixed.only=FALSE needed for dataCl.
## Maximum Likelihood (ML), and "monitor" iterations via 'verbose':
fm1_ML < update(fm1, REML=FALSE, verbose = 1)
(fm2 < lmer(Reaction ~ Days + (Days  Subject), sleepstudy))
anova(fm1, fm2)
sm2 < summary(fm2)
print(fm2, digits=7, ranef.comp="Var") # the print.merMod() method
print(sm2, digits=3, corr=FALSE) # the print.summary.merMod() method
(vv < vcov.merMod(fm2, corr=TRUE))
as(vv, "corMatrix")# extracts the ("hidden") 'correlation' entry in @factors
## Fit sexspecific variances by constructing numeric dummy variables
## for sex and sex:age; in this case the estimated variance differences
## between groups in both intercept and slope are zero ...
data(Orthodont,package="nlme")
Orthodont$nsex < as.numeric(Orthodont$Sex=="Male")
Orthodont$nsexage < with(Orthodont, nsex*age)
lmer(distance ~ age + (ageSubject) + (0+nsexSubject) +
(0 + nsexageSubject), data=Orthodont)