nlsModel {stats} | R Documentation |

This is the constructor for `nlsModel`

objects, which are
function closures for several functions in a list. The closure
includes a nonlinear model formula, data values for the formula, as
well as parameters and their values.

nlsModel(form, data, start)

`form` |
a nonlinear model formula |

`data` |
a data frame or a list in which to evaluate the variables from the model formula |

`start` |
a named list or named numeric vector of starting estimates for the parameters in the model |

An `nlsModel`

object is primarily used within the `nls`

function. It encapsulates the model, the data, and the parameters in
an environment and provides several methods to access characteristics
of the model. It forms an important component of the object returned
by the `nls`

function.

See `nls`

for where elements of the formula `form`

are looked for. In normal use all the variables will be in `data`

.

The value is a list of functions that share a common environment.

`resid` |
returns the residual vector evaluated at the current parameter values |

`fitted` |
returns the fitted responses and their gradient at the current parameter values |

`formula` |
returns the model formula |

`deviance` |
returns the residual sum-of-squares at the current parameter values |

`gradient` |
returns the gradient of the model function at the current parameter values |

`conv` |
returns the relative-offset convergence criterion evaluated at the current parmeter values |

`incr` |
returns the parameter increment calculated according to the Gauss-Newton formula |

`setPars` |
a function with one argument, `pars` . It sets the
parameter values for the `nlsModel` object and returns a
logical value denoting a singular gradient array. |

`getPars` |
returns the current value of the model parameters as a numeric vector |

`getAllPars` |
returns the current value of the model parameters as a numeric vector |

`getEnv` |
returns the environment shared by these functions,
which contains copies of all the variables in `data` and has
as parent the environment of `form` . |

`trace` |
the function that is called at each iteration if tracing is enabled |

`Rmat` |
the upper triangular factor of the gradient array at the current parameter values |

`predict` |
takes as argument `newdata` ,a `data.frame` and
returns the predicted response for `newdata` . |

Douglas M. Bates and Saikat DebRoy

Bates, D.M. and Watts, D.G. (1988), *Nonlinear Regression Analysis
and Its Applications*, Wiley

DNase1 <- DNase[ DNase$Run == 1, ] mod <- nlsModel(density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1, list( Asym = 3, xmid = 0, scal = 1 )) mod$getPars() # returns the parameters as a list mod$deviance() # returns the residual sum-of-squares mod$resid() # returns the residual vector and the gradient mod$incr() # returns the suggested increment mod$setPars( unlist(mod$getPars()) + mod$incr() ) # set new parameter values mod$getPars() # check the parameters have changed mod$deviance() # see if the parameter increment was successful mod$trace() # check the tracing mod$Rmat() # R matrix from the QR decomposition of the gradient

[Package *stats* version 2.1.0 Index]