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)$boundary.tol,
start = NULL, verbose = 0L,
control = list(), ...)
glFormula(formula, data = NULL, family = gaussian,
subset, weights, na.action, offset, contrasts = NULL,
start, mustart, etastart, control = glmerControl(), ...)
mkGlmerDevfun(fr, X, reTrms, family, nAGQ = 1L,
verbose = 0L, maxit = 100L, control = glmerControl(), ...)
optimizeGlmer(devfun, optimizer = "bobyqa",
restart_edge = FALSE,
boundary.tol = formals(glmerControl)$boundary.tol,
verbose = 0L, control = list(),
nAGQ = 1L, stage = 1, start = NULL, ...)
updateGlmerDevfun(devfun, reTrms, nAGQ = 1L)
~
operator and the terms, separated by + operators,
on the right. Random-effects terms are distinguished by
vertical bars ("|") separating expressions for
design matrices from grouping factors.formula. By default the
variables are taken from the environment from which
lmer is called. While data is optional, the
package authors strongly recommend its use,
especially when later applying methods such as
update and drop1 to the fitted model
(such methods are not guaranteed to work properly
if data is omitted). If data is omitted,
variables will be taken from the environment of
formula (if specified as a formula) or from the
parent frame (if specified as a character vector).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.NULL or a numeric vector.NAs. The default
action (na.omit, inherited from the 'factory
fresh' value of getOption("na.action")) strips any
observations with any missing values in any variables.NULL or a numeric vector of length
equal to the number of cases. One or more offset
terms can be included in the formula instead or as well, and if
more than one is specified their sum is used. See
model.offset.list. See the
contrasts.arg of model.matrix.default.[g]lFormula:lmerControl;mkLmerDevfun,mkGlmerDevfun:optimizeLmer and optimizeGlmer:lmerControl).
mkReTrms).lmer;
for glFormula, should be just a numeric vector of
fixed-effect coefficients)mkLmerDevfunlmer or glmer, possibly length
2 for glmer. The built-in optimizers are
"Nelder_Mead" and "bobyqa"
(from the minqa package). Any minimizing function
that allows box constraints can be used provided that it
fn (function to be
optimized), par (starting parameter values),
lower (lower bounds) and control (control
parameters, passed through from the control
argument) and
par (best-fit parameters), fval (best-fit
function value), conv (convergence code) and
(optionally) message (informational message, or
explanation of convergence failure).
Special provisions are made for bobyqa,
Nelder_Mead, and optimizers wrapped in the
optimx package; to use optimx optimizers
(including L-BFGS-B from base optim
and nlminb), pass the method
argument to optim in the control argument.
For glmer, if length(optimizer)==2, the
first element will be used for the preliminary (random
effects parameters only) optimization, while the second
will be used for the final (random effects plus fixed
effect parameters) phase. See modular for
more information on these two phases.
lmerControllmerControlglm for details.glm for details.optimizeLmer and
optimizeGlmer, these are passed to internal function
optwrap, which has relevant parameters calc.derivs
and use.last.params (see lmerControl).lFormula and glFormula return a list containing
components: mkReTrmsmkLmerDevfun 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.[g]lFormula takes 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)merDevfun takes the output of the previous
step (minus the formula component) and creates a
deviance function
optimize(Gl|L)mer takes a
deviance function and optimizes over theta (or
over theta and beta, if stage is set
to 2 for optimizeGlmer
updateGlmerDevfun takes the first stage of a GLMM
optimization (with nAGQ=0, optimizing over theta only)
and produces a second-stage deviance function
mkMerMod takes the environment of 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]lmerMod object.
### 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)
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