nlmer: Fit Nonlinear Mixed Models

Description

Fit a nonlinear mixed model with nested or crossed grouping factors for the random effects.

Usage

nlmer(formula, data, control, start, verbose,
     subset, weights, na.action, contrasts,
     model, ...)

Arguments

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 mod

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 star

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.

Value

An object of class "nlmer". There are many methods applicable to "nlmer" objects, see the above help page.

Details

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. [object Object],[object Object],[object Object],[object Object]

Examples

Run this code

(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))