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, 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, subset, weights, na.action, offset, contrasts = NULL, model = TRUE, control = list(), ...) nlmer(formula, data, start = NULL, verbose = FALSE, nAGQ = 1, 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
~ operator and the terms, separated by + operators, on
the right. The vertical bar character "|" separates an
expression for a model matrix and a grouping factor. |
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. |
family |
a GLM family, see glm and
family . If family is missing then a
linear mixed model is fit; otherwise a generalized linear mixed
model is fit. |
REML |
logical argument to lmer only. Should the estimates
be chosen to optimize the REML criterion (as opposed to the
log-likelihood)? Defaults to TRUE. |
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 ST slot, it is
becomes the starting values of the ST slot. It the list
contains components named fixef and/or ST , these are
used as the starting values for those slots. (Setting starting
values for fixef has no effect for a linear mixed model
because the fixed-effects parameters do not appear in the profiled
deviance.) In lmer and glmer a numeric
start argument of the appropriate length is used as the
starting value of the parameter vector that determines the ST
slot. In nlmer a numeric start argument is used as the
starting values of the fixef slot. |
subset, weights, na.action, offset, contrasts |
further model
specification arguments as in lm ; see there for
details. |
model |
logical scalar. If FALSE the model frame in
slot frame is truncated to zero rows. |
x |
logical scalar. If FALSE the model matrix in
slot X is truncated to zero rows. |
verbose |
logical scalar. If TRUE verbose output is
generated during the optimization of the parameter estimates. |
... |
other potential arguments. A method argument was
used in earlier versions of the package. It's functionality has been
replaced by the REML and nAGQ arguments. |
This is a revised version of the lme
function from the
nlme package. This version uses a different method of
specifying random-effects terms and allows for fitting generalized
linear mixed models and nonlinear mixed models in addition to linear
mixed models.
Additional standard arguments to model-fitting functions can be passed
to lmer
.
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.NA
s. The default action (na.fail
) prints
an error message and terminate if there are any incomplete
observations.msVerbose
:trace
argument to nlminb
(see documentation on
that function). Default is getOption("verbose")
.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)) ## nonlinear mixed models (nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = c(Asym = 200, xmid = 725, scal = 350)))