R: Nonlinear Least Squares

nls {nls}

R Documentation

Nonlinear Least Squares

Description

Determine the nonlinear least squares estimates of the parameters.

Usage

nls(formula, data, start, control=nls.control(),
    algorithm="default", trace=F, subset, na.action)

Arguments

formula

a nonlinear model formula including variables and parameters

data

an optional data frame in which to evaluate the variables in formula

start

a named list or named numeric vector of starting estimates

control

an optional list of control settings. See nlsControl for the names of the settable control values and their effect.

algorithm

character string specifying the algorithm to use. The default algorithm is a Gauss-Newton algorithm. The other alternative is "plinear", the Golub-Pereyra algorithm for partially linear least-squares models.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

na.action

a function which indicates what should happen when the data contain NAs.

Details

An nls object is a type of fitted model object. It has methods for the generic functions coef, formula, resid, print, summary, and fitted.

Value

A list of

m

an nlsModel object incorporating the model

data

the expression that was passed to nls as the data argument. The actual data values are present in the environment of the m component.

Author(s)

Douglas M. Bates and Saikat DebRoy

References

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

Examples

library( nls )
data( DNase )
DNase1 <- DNase[ DNase$Run == 1, ]
## using a selfStart model
fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 )
summary( fm1DNase1 )
## using conditional linearity
fm2DNase1 <- nls( density ~ 1/(1 + exp(( xmid - log(conc) )/scal ) ),
                  data = DNase1,
                  start = list( xmid = 0, scal = 1 ),
                  alg = "plinear", trace = TRUE )
summary( fm2DNase1 )
## without conditional linearity
fm3DNase1 <- nls( density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
                  data = DNase1,
                  start = list( Asym = 3, xmid = 0, scal = 1 ),
                  trace = TRUE )
summary( fm3DNase1 )