nl.lmsNM: Fitt a nonlinear regression model by least median of squares....

In nlr: Nonlinear Regression Modelling using Robust Methods

Description

Least Median of square estimate is robust fitt by minimizing the median of squared residuals. This function use the "optim" function which minimize using Golden section method.

Usage

nl.lmsNM(formula, data, start)

Arguments

formula	nl.form object of the nonlinear function model.
data	list of data include responce and predictor.
start	list of parameter values of nonlinear model function (θ. in f(x,θ)).

Details

Note that due to using classic optimization method, and due to non uniqueness of minimum of objective function this estimate is not efficient, but is high breakdown estimate.

Value

list of parameter estimates.

Note

A more efficient function is recomended is nl.lmsGA. When that function is not working properly this function can be used, but requires more precise initial values. These function uses by nlr for initial purposes. User can request least median square as initial value from nlr.control argument by initials="lms" method, nlr use nl.lmsGA not nl.lmsNM.

Author(s)

Hossein Riazoshams, May 2014. Email: [e-mail] URL http://www.riazoshams.com/nlr/

References

Riazoshams H, Midi H, and Ghilagaber G, 2018,. Robust Nonlinear Regression, with Application using R, Joh Wiley and Sons.

See Also

nl.lmsGA, nlr.control, nlr

Examples

  # chicken data fitt example
  data=list(xr=Weights$Date,yr=Weights$Weight)
  fit<- nl.lmsNM(nlrobj1[[14]],data=data,start=list(p1=1000,p2=42,p3=.11))
  fit

nlr documentation built on July 31, 2019, 5:09 p.m.