List of nls Objects with a Common Model

DESCRIPTION:

Data is partitioned according to the levels of the grouping factor defined in model and individual nls fits are obtained for each data partition, using the model defined in model .

USAGE:

nlsList(model, data, start, control, level, na.action, pool) 

REQUIRED ARGUMENTS:

model
either a nonlinear model formula, with the response on the left of a `~' operator and an expression involving parameters, covariates, and a grouping factor separated by the | operator on the right, or a selfStart function. The method function nlsList.selfStart is documented separately.
data
a data frame in which to interpret the variables named in model.

OPTIONAL ARGUMENTS:

start
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.
control
a list of control values passed as the control argument to nls. Defaults to an empty list.
level
an optional integer specifying the level of grouping to be used when multiple nested levels of grouping are present.
na.action
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.
pool
an optional logical value that is preserved as an attribute of the returned value. This will be used as the default for pool in calculations of standard deviations or standard errors for summaries.

VALUE:

a list of nls objects with as many components as the number of groups defined by the grouping factor. 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.

WARNING:

If one of the individual nls fits fails, an ERROR message will appear. The remainder models will still be fitted assigning NAs to the coefficients of the model for the group whose fit failed.

The computational method used to accomplish the recursive fitting of the individual models to the groups regardless of one of them failing, will sometimes result on multiple errors if a mistake is made on model specification, for example. A pervasive error of this type will appear as many times as there are groups in the data frame.

SEE ALSO:

, , , , .

EXAMPLES:

fm1 <- nlsList(weight ~ SSlogis(Time, Asym, xmid, scal) | Plot, Soybean)