lme4 (version 1.1-7)

modular: Modular functions for mixed model fits

Description

Modular functions for mixed model fits

Usage

lFormula(formula, data = NULL, REML = TRUE, subset,
    weights, na.action, offset, contrasts = NULL,
    control = lmerControl(), ...)
mkLmerDevfun(fr, X, reTrms, REML = TRUE, start = NULL,
    verbose = 0, control = lmerControl(), ...)
optimizeLmer(devfun,
             optimizer    = formals(lmerControl)$optimizer,
             restart_edge = formals(lmerControl)$restart_edge,
             boundary.tol = formals(lmerControl)$boundary.tol,
             start = NULL, verbose = 0L,
             control = list(), ...)
glFormula(formula, data = NULL, family = gaussian,
    subset, weights, na.action, offset, contrasts = NULL,
    mustart, etastart, control = glmerControl(), ...)
mkGlmerDevfun(fr, X, reTrms, family, nAGQ = 1L,
    verbose = 0L, control = glmerControl(), ...)
optimizeGlmer(devfun, optimizer = "bobyqa",
    restart_edge = FALSE,
    boundary.tol = formals(glmerControl)$boundary.tol,
    verbose = 0L, control = list(),
    nAGQ = 1L, stage = 1, start = NULL, ...)
updateGlmerDevfun(devfun, reTrms, nAGQ = 1L)

Arguments

formula

a two-sided linear formula object describing both the fixed-effects and fixed-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right. Random

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 st

REML

(logical) indicating to fit restricted maximum likelihood model.

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

weights

an optional vector of prior weights to be used in the fitting process. Should be NULL or a numeric vector.

na.action

a function that indicates what should happen when the data contain NAs. The default action (na.omit, inherited from the 'factory fresh' value of getOption("na.action")) strips any observations with a

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

contrasts

an optional list. See the contrasts.arg of model.matrix.default.

control

a list giving [object Object],[object Object],[object Object]

fr

A model frame containing the variables needed to create an lmerResp or glmResp instance.

X

fixed-effects design matrix

reTrms

information on random effects structure (see mkReTrms).

start

starting values (see lmer)

verbose

print output?

devfun

a deviance function, as generated by mkLmerDevfun

nAGQ

number of Gauss-Hermite quadrature points

stage

optimization stage (1: nAGQ=0, optimize over theta only; 2: nAGQ possibly >0, optimize over theta and beta)

optimizer

character - name of optimizing function(s). A character vector or list of functions: length 1 for lmer or glmer, possibly length 2 for glmer. The built-in optimizers are "

Value

• lFormula and glFormula return a list containing components: [object Object],[object Object],[object Object],[object Object]

  mkLmerDevfun and mkGlmerDevfun return a function to calculate deviance (or restricted deviance) as a function of the theta (random-effect) parameters. updateGlmerDevfun returns a function to calculate the deviance as a function of a concatenation of theta and beta (fixed-effect) parameters. These deviance functions have an environment containing objects required for their evaluation. CAUTION: The environment of functions returned by mk(Gl|L)merDevfun contains reference class objects (see ReferenceClasses, merPredD-class, lmResp-class), which behave in ways that may surprise many users. For example, if the output of mk(Gl|L)merDevfun is naively copied, then modifications to the original will also appear in the copy (and vice versa). To avoid this behavior one must make a deep copy (see ReferenceClasses for details).

  optimizeLmer and optimizeGlmer return the results of an optimization.

item

• restart_edge
• boundary.tol
• family
• mustart
• etastart
• ...

code

lmerControl

Details

These functions make up the internal components of an [gn]lmer fit.

• [g]lFormulatakes the arguments that would normally be passed to[g]lmer, checking for errors and processing the formula and data input to create a list of objects required to fit a mixed model.
• mk(Gl|L)merDevfuntakes the output of the previous step (minus theformulacomponent) and creates a deviance function
• optimize(Gl|L)mertakes a deviance function and optimizes overtheta(or overthetaandbeta, ifstageis set to 2 foroptimizeGlmer
• updateGlmerDevfuntakes the first stage of a GLMM optimization (withnAGQ=0, optimizing overthetaonly) and produces a second-stage deviance function
• mkMerModtakes theenvironmentof a deviance function, the results of an optimization, a list of random-effect terms, a model frame, and a model all and produces a[g]lmerModobject.

Examples

### Fitting a linear mixed model in 4 modularized steps

## 1.  Parse the data and formula:
lmod <- lFormula(Reaction ~ Days + (Days|Subject), sleepstudy)
names(lmod)
## 2.  Create the deviance function to be optimized:
(devfun <- do.call(mkLmerDevfun, lmod))
ls(environment(devfun)) # the environment of 'devfun' contains objects
                        # required for its evaluation
## 3.  Optimize the deviance function:
opt <- optimizeLmer(devfun)
opt[1:3]
## 4.  Package up the results:
mkMerMod(environment(devfun), opt, lmod$reTrms, fr = lmod$fr)


### Same model in one line
lmer(Reaction ~ Days + (Days|Subject), sleepstudy)


### Fitting a generalized linear mixed model in six modularized steps

## 1.  Parse the data and formula:
glmod <- glFormula(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial)
names(glmod)
## 2.  Create the deviance function for optimizing over theta:
(devfun <- do.call(mkGlmerDevfun, glmod))
ls(environment(devfun)) # the environment of devfun contains lots of info
## 3.  Optimize over theta using a rough approximation (i.e. nAGQ = 0):
(opt <- optimizeGlmer(devfun))
## 4.  Update the deviance function for optimizing over theta and beta:
(devfun <- updateGlmerDevfun(devfun, glmod$reTrms))
## 5.  Optimize over theta and beta:
opt <- optimizeGlmer(devfun, stage=2)
opt[1:3]
## 6.  Package up the results:
mkMerMod(environment(devfun), opt, glmod$reTrms, fr = glmod$fr)

### Same model in one line
glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
      data = cbpp, family = binomial)