nls {nls}

R Documentation

Nonlinear Least Squares

Description

Determine the nonlinear least squares estimates of the nonlinear model parameters and return a class nls object.

Usage

nls(formula, data = parent.frame(), start, control = nls.control(),
    algorithm = "default", trace = FALSE, subset,
    weights, 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 nls.control 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.
trace	logical value indicating if a trace of the iteration progress should be printed. Default is FALSE. If TRUE the residual sum-of-squares and the parameter values are printed at the conclusion of each iteration. When the "plinear" algorithm is used, the conditional estimates of the linear parameters are printed after the nonlinear parameters.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
weights	an optional numeric vector of (fixed) weights. When present, the objective function is weighted least squares. not yet implemented
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, AIC, 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

See Also

nlsModel

Examples

data( DNase )
DNase1 <- DNase[ DNase$Run == 1, ]
## using a selfStart model
fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 )
summary( fm1DNase1 )
## using conditional linearity
fm2DNase1 <- nls( density ~ 1/(1 + exp(( xmid - log(conc) )/scal ) ),
                  data = DNase1,
                  start = list( xmid = 0, scal = 1 ),
                  alg = "plinear", trace = TRUE )
summary( fm2DNase1 )
## without conditional linearity
fm3DNase1 <- nls( density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
                  data = DNase1,
                  start = list( Asym = 3, xmid = 0, scal = 1 ),
                  trace = TRUE )
summary( fm3DNase1 )
## weighted nonlinear regression
data(Puromycin)
Treated <- Puromycin[Puromycin$state == "treated", ]
weighted.MM <- function(resp, conc, Vm, K)
{
    ## Purpose: exactly as white book p.451 -- RHS for nls()
    ##  Weighted version of Michaelis-Menten model
    ## -------------------------------------------------------------------------
    ## Arguments: `y', `x' and the two parameters (see book)
    ## -------------------------------------------------------------------------
    ## Author: Martin Maechler, Date: 23 Mar 2001, 18:48

    pred <- (Vm * conc)/(K + conc)
    (resp - pred) / sqrt(pred)
}

Pur.wt <- nls( ~ weighted.MM(rate, conc, Vm, K), data = Treated,
              start = list(Vm = 200, K = 0.1),
              trace = TRUE)