Method nls

Description

For a given forumla (describing a model) and data this method applies the simple non- linear least squares algorithm - this calculates the parameters that minimisethe sum of squares difference between the observed (data) and predicted (model) values.

The algorithm can be sensitive to the initial values of the problem so do try different start points and check they converge to the same estimates.

Generic function

nls(formula,data,start,control,algorithm,trace,subset,weights,na.action,model,lower,upper)

Methods

signature(formula=FLModel,data=missing,start=missing,control=missing,algorithm=missing,trace=missing,subset=missing,weights=missing,na.action=missing,model=missing,lower=missing,upper=missing) :

Applies non-linear sum of squares to the model and data in the input FLModel-class object.

Author(s)

The FLR Team

See Also

FLComp

Examples

# An example FLSR (FLModel) object
data(nsher)

#set bevholt model
model(nsher) <- bevholt

# fit through nls
nsher <- nls(nsher)

summary(nsher)