Usage
nlmer(formula, data = NULL, control = nlmerControl(),
start = NULL, verbose = 0L, nAGQ = 1L, subset, weights,
na.action, offset, contrasts = NULL,
devFunOnly = FALSE, ...)
Arguments
formula
a nonlinear mixed model formula (see
detailed documentation)
start
starting estimates for the nonlinear model
parameters, as a named numeric vector or as a list with
components [object Object],[object Object]
...
other potential arguments. A method
argument was used in earlier versions of the package. Its
functionality has been replaced by the nAGQ
argument.
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 strongly
control
a list (of correct class, resulting from
lmerControl()
or
glmerControl()
respectively) containing
control parameters, including the nonlinear verbose
integer scalar. If > 0
verbose
output is generated during the optimization of the
parameter estimates. If > 1
verbose output is
generated during the individual PIRLS steps.
nAGQ
integer scalar - the number of points per
axis for evaluating the adaptive Gauss-Hermite
approximation to the log-likelihood. Defaults to 1,
corresponding to the Laplace approximation. Values
greater than 1 produce greater accuracy in the evalua
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 of the r
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 NA
s. The default
action (na.omit
, inherited from the 'factory
fresh' value of getOption("na.action")
) strips any
observations with any missi
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
.
devFunOnly
logical - return only the deviance
evaluation function. Note that because the deviance
function operates on variables stored in its environment,
it may not return exactly the same values on
subsequent calls (but the results should always