nlmer {lme4} | R Documentation |
Fit a nonlinear mixed model with nested or crossed grouping factors for the random effects.
nlmer(formula, data, control, start, verbose, subset, weights, na.action, contrasts, model, ...)
formula |
a three part formula object describing the response,
the nonlinear model and the fixed and random effects in the model.
In the fixed and random effects specification the vertical bar
character "|" separates an expression for a model matrix and
a grouping factor. At present evaluation of the nonlinear model
function must return a gradient attribute. |
data |
an optional data frame containing the variables named in
formula . By default the variables are taken from the
environment from which nlmer is called. |
control |
a list of control parameters. See below for details. |
start |
a named numeric vector of starting values for the fixed
effects parameters or a list that contains an element called
"fixef" that has this form. Optionally the list may contain
an element called "ST" providing a starting value for the
ST slot. |
verbose |
logical scalar - TRUE indicates verbose output
from the iterations during the optimization process (highly
recommended when difficulties are encountered). Default is FALSE . |
subset, weights, na.action, contrasts |
further model
specification arguments as in lm ; see there for
details. |
model |
logical indicating if the model component
should be returned (in slot frame ). |
... |
potentially further arguments for methods. Currently none are used. |
This is a revised version of the nlme
function from the
nlme package. This version uses a different method of
specifying random-effects terms and allows for fitting generalized
linear mixed models as well as 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
) causes
lme
to print an error message and terminate if there are any
incomplete observations.maxIter
:lme
optimization algorithm. Default is 50.tolerance
:lme
optimization algorithm. Default is
sqrt(.Machine$double.eps)
.msMaxIter
:nlminb
optimization step inside the lme
optimization. Default is 200.msVerbose
:trace
argument to nlminb
(see documentation on
that function). Default is getOption("verbose")
.niterEM
:EMverbose
:getOption("verbose")
.PQLmaxIt
:usePQL
:method = "Laplace"
? Default is FALSE
.TRUE
the corresponding
components of the fit (the model frame, the model matrices)
are returned.
An object of class "nlmer"
.
There are many methods applicable to "nlmer"
objects, see the
above help page.
The nlmer
class
(fm1 <- lme4:::nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, verb = 1, start = c(Asym = 192.6872, xmid = 728.7544, scal = 353.5320))) (fm2 <- lme4:::nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKe+lKa+lCl|Subject), Theoph, start = c(lKe = -2.5, lKa = 0.5, lCl = -3), verb = 1)) (fm3 <- lme4:::nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKe|Subject) + (lKa|Subject) + (lCl|Subject), Theoph, start = c(lKe = -2.5, lKa = 0.5, lCl = -3), verb = 1)) (fm4 <- lme4:::nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKa+lCl|Subject), Theoph, start = c(lKe = -2.5, lKa = 0.5, lCl = -3), verb = 1)) (fm5 <- lme4:::nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKa|Subject) + (lCl|Subject), Theoph, start = c(lKe = -2.5, lKa = 0.5, lCl = -3), verb = 1))