Fit a Nonlinear Mixed Effects Model
DESCRIPTION:
A method for the generic function nlme() for objects inheriting from class nlsList.
USAGE: 
nlme.nlsList(object, fixed, random = fixed, cluster, data=sys.parent(),
             start, est.method=c("ML", "RML"), re.block,
             re.structure=c("unstructured", "diagonal", "identity",
             "compsymm", "ar1"), re.paramtr=c("matrixlog", "cholesky",
             "spherical", "logcholesky", "givens"),
             serial.structure=c("identity", "ar1", "ar1.continuous",
             "compsymm", "ar2", "ma1", "ma2", , serial.covariate=NULL,
             serial.covariate.transformation=c("rank.within.cluster",
             "none", "round", "global.rank"), var.function=c("identity",
             "power", "expon", "cte.power", "cte.expon"),
             var.covariate=NULL, var.estimate=T, control, na.action,
             na.pattern, verbose=F)
REQUIRED ARGUMENTS:
object:
an object inheriting from class nlsList.
OPTIONAL ARGUMENTS:
fixed:
cluster:
data:
start:
these arguments, though included in the calling sequence to be consistent with the generic function call, should not be supplied in the call to nlme.nlsList() as they are obtained from object. Initial values given to these arguments will be overwritten with those obtained from object.
others:
see the documentation on nlme.formula.
VALUE:
an object of class nlme representing the fit. Generic functions such as print, plot, predict, and summary have methods to show the results of the fit. See nlme.object for the components of the fit. The functions residuals, coefficients and fitted.values can be used to extract some of its components.
SEE ALSO:
nlsList , nlme.formula , nlme.control , nlme.object .
EXAMPLES: 
Theo.nlsList <- nlsList(conc ~ first.order.log(Dose, time, lCl, lka,
                          lke), data = Theoph, cluster = ~ Subject)


Theo.nlsList.fit <- nlme(Theo.nlsList)


Theo.nlsList.fit


# Returns the following:
Call:
  Model: conc ~ first.order.log(Dose, time, lCl, lka, lke)
  Fixed: list(lCl ~ ., lka ~ ., lke ~ .)
 Random: list(lCl ~ ., lka ~ ., lke ~ .)
Cluster:  ~ Subject
   Data: Theoph


Variance/Covariance Components Estimate(s):


  Structure: matrixlog
  Standard Deviation(s) of Random Effect(s)
       lCl       lka       lke
 0.2510528 0.6364656 0.1309519
 Correlation of Random Effects
            lCl         lka
lka -0.08624075
lke  0.99477586  0.015542499


 Cluster Residual Variance: 0.4650816


Fixed Effects Estimate(s):
        lCl       lka       lke
  -3.214444 0.4512505 -2.432664


Number of Observations: 132
Number of Clusters: 12