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`nlsList.selfStart` nlsList Fit from a selfStart Function

Description

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.

Usage

## S3 method for class 'selfStart'
nlsList(model, data, start, control, level, subset,
        na.action = na.fail, pool = TRUE, warn.nls = NA)

Arguments

`model`	a `"selfStart"` model function, which calculates initial estimates for the model parameters from `data`.
`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"`.
`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.
`subset`	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.
`na.action`	a function that indicates what should happen when the data contain `NA`s. The default action (`na.fail`) causes `nlsList` to print an error message and terminate if there are any incomplete observations.
`pool, warn.nls`	optional `logical`s, see `nlsList`.

Value

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.

Examples

fm1 <- nlsList(SSasympOff, CO2)
summary(fm1)