nl.lts: Compute (LTS) Least Trimmed Square Estimate.

In nlr: Nonlinear Regression Modelling using Robust Methods

Description

LTS is minimizing trimmed sum of squares.

Usage

nl.lts(formula, data, start, h = NULL, control = nlr.control())

Arguments

formula	nl.form object of the nonlinear model function.
data	List of data used in predictor and response.
start	List of parameter starting value.
h	Percentage of trimming the residuals, if omited the default 25% will be used.
control	nlr.control options, will be submited to least square nlsqr function.

Details

LTS trimme h percent of residuals first then compute the least square estimate, and final parameter estimate is the one minimize the sum of squares of errors.

Value

nl.fitt object of fitted model parameter.

Note

The result data returnd in fitted object is trimmed data.

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.form, nl.fitt

Examples

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

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