lmer {lme4} | R Documentation |
Fit a linear mixed model or a generalized linear mixed model or a nonlinear mixed model.
lmer(formula, data, family = NULL, REML = TRUE, control = list(), start = NULL, verbose = FALSE, doFit = TRUE, subset, weights, na.action, offset, contrasts = NULL, model = TRUE, x = TRUE, ...) lmer2(formula, data, family = NULL, REML = TRUE, control = list(), start = NULL, verbose = FALSE, subset, weights, na.action, offset, contrasts = NULL, model = TRUE, x = TRUE, ...) glmer(formula, data, family = gaussian, start = NULL, verbose = FALSE, nAGQ = 1, doFit = TRUE, subset, weights, na.action, offset, contrasts = NULL, model = TRUE, control = list(), ...) nlmer(formula, data, start = NULL, verbose = FALSE, nAGQ = 1, doFit = TRUE, subset, weights, na.action, contrasts = NULL, model = TRUE, control = list(), ...)
formula |
a two-sided linear formula object describing the
fixed-effects part of the model, with the response on the left of a
|
data |
an optional data frame containing the variables named in
|
family |
a GLM family, see |
REML |
logical argument to |
nAGQ |
a positive integer - the number of points per axis for evaluating the adaptive Gauss-Hermite approximation to the log-likelihood. This defaults to 1, corresponding to the Laplacian approximation. Values greater than 1 produce greater accuracy in the evaluation of the log-likelihood at the expense of speed. |
control |
a list of control parameters. See below for details. |
start |
a named list of starting values for the parameters in the
model. If the list is of the same form as the |
doFit |
logical scalar. When |
subset, weights, na.action, offset, contrasts |
further model
specification arguments as in |
model |
logical scalar. If |
x |
logical scalar. If |
verbose |
logical scalar. If |
... |
other potential arguments. A |
The lmer
and glmer
functions are nearly interchangeable.
If lmer
is called with a non-default family
argument the
call is replaced by a call to glmer
with the current arguments.
If glmer
is called with the default family
, namely the
gaussian
family with the identity link,
then the call is replaced by a call to lmer
with the current
arguments. (They are described as “nearly” interchangeable
because the REML
argument only applies to calls to lmer
and the nAGQ
argument only applies to calls to glmer
.)
Additional standard arguments to model-fitting functions can be passed
to lmer
or glmer
or nlmer
:
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.
a function that indicates what should happen when the
data contain NA
s. The default action (na.fail
) prints
an error message and terminate if there are any incomplete
observations.
a named list of control parameters for the estimation
algorithm, specifying only the ones to be changed from their
default values. Hence defaults to an empty list.
Possible control options and their default values are:
msVerbose
:a logical value passed as the
trace
argument to nlminb
(see documentation on
that function). Default is getOption("verbose")
.
maxIter
:a positive integer passed as the
maxIter
argument to nlminb
(see documentation on
that function). Default is 300
.
maxFN
:a positive integer specifying the
maximum number of evaluations of the deviance function allowed
during the optimization. Default is 900
.
logicals. If TRUE
the corresponding
components of the fit (the model frame, the model matrices)
are returned.
The lmer2
name exists only for backwards compatibility.
Calling this function simply produces an equivalent call to lmer
.
An object of class "mer"
, for which many methods
are available. See there for details.
## linear mixed models (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) anova(fm1, fm2) ## generalized linear mixed model (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp)) ## GLMM with individual-level variability (accounting for overdispersion) cbpp$obs <- 1:nrow(cbpp) (gm2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd) + (1|obs), family = binomial, data = cbpp)) anova(gm1,gm2) ## nonlinear mixed models (nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = c(Asym = 200, xmid = 725, scal = 350)))