The response variable and primary covariate in formula(data)
are used together with model to construct the nonlinear model
formula. This is used in the nls calls and, because a
selfStarting model function can calculate initial estimates for its
parameters from the data, no starting estimates need to be provided.
# S3 method for selfStart
nlsList(model, data, start, control, level, subset,
na.action = na.fail, pool = TRUE, warn.nls = NA)a list of nls objects with as many components as the number of
groups defined by the grouping factor. A NULL value is assigned
to the components corresponding to clusters for which the nls
algorithm failed to converge. Generic functions such as coef,
fixed.effects, lme, pairs, plot,
predict, random.effects, summary, and
update have methods that can be applied to an nlsList
object.
a "selfStart" model function, which calculates
initial estimates for the model parameters from data.
a data frame in which to interpret the variables in
model. Because no grouping factor can be specified in
model, data must inherit from class
"groupedData".
an optional named list with initial values for the
parameters to be estimated in model. It is passed as the
start argument to each nls call and is required when
the nonlinear function in model does not inherit from class
selfStart.
a list of control values passed as the control
argument to nls. Defaults to an empty list.
an optional integer specifying the level of grouping to be used when multiple nested levels of grouping are present.
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 row names to be
included. All observations are included by default.
a function that indicates what should happen when the
data contain NAs. The default action (na.fail) causes
nlsList to print an error message and terminate if there are any
incomplete observations.
optional logicals, see nlsList.
fm1 <- nlsList(SSasympOff, CO2)
summary(fm1)
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