lme4 (version 0.999375-28)

lmer: Fit Mixed-Effects Models

Description

Fit a linear mixed model or a generalized linear mixed model or a nonlinear mixed model.

Usage

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(), ...)

Arguments

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

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 m

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 accurac

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. If the list contains components named fixef

doFit

logical scalar. When doFit = FALSE the model is not fit but instead a structure with the model matrices for the random-effects terms is returned, so they can be modified for special model forms. When doFit = TRUE, the

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. Its functionality has been replaced by the REML and nAGQ arguments.

Value

• An object of class "mer", for which many methods are available. See there for details.

concept

• GLMM
• NLMM

Details

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: [object Object],[object Object],[object Object],[object Object]

The lmer2 name exists only for backwards compatibility. Calling this function simply produces an equivalent call to lmer.

See Also

The mer class, lm

Examples

## 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)))