nlout.JL: Nonlinear outlier detection.

In nlr: Nonlinear Regression Modelling using Robust Methods

Description

Detecting outlier for nonlinear regression, is based on mixing statsitics measures and robust estimates through their covariance matrices (hat matrix). The covariance matrix in nonlinear is based on the gradient of nonlinear regression model, but it based on linear approximation of the model, instead Jacobian Leverage is used in this function.

Usage

nlout.JL(nlfited)

Arguments

nlfited

Object of type nl.fitt or nl.fitt.gn for classic estimators, nl.fitt.rob or nl.fitt.rgn for robust estimators.

Details

The outlier detection measutred used in this function are studentized residuals and Cook Distance. They are mixture of estimators and Jacobians. They are successful for detecting outlier only if combine with robust fits, eventhough the function can work with classic fits but it is not recomended. Riazoshams and Midi (2014)

Value

Result is list of nl.robmeas objects for each statistics.

"jl.vmat"	Jacobian-leverage matrix.
"jl.studres"	nl.robmeas object of Jacobian Leverage Studentised Residuals.
"jl.cook"	nl.robmeas object of Jacobian Leverage Elliptic Norm (Cook Dist).
"jl.hadi"	nl.robmeas object of Jacobian Leverage Hadi potential.
"jl.delstud"	nl.robmeas object of Jacobian Leverage Deletion Studentized.
"jl.dffits"	nl.robmeas object of Jacobian Leverage DFFITS.
"jl.atk"	nl.robmeas object ofJacobian Leverage Atkinson Distance.

Note

This function return back all resutls and statistics based on Jacobian leverage, but Riazoshams (2014) showed studentized residuals when combine with robust estimators can detect outliers correctly. Thus to identify outlier correctly first estimate the parameters bu robust options of nlr function then call nlout, finally look at the list values "jl.delstud" from the result list. The plot and other methods of nl.robmeas display the results visually.

Author(s)

Hossein Riazoshams, Jan 2010. Email: [e-mail] URL http://www.riazoshams.com/nlr/

References

Riazoshams H, Habshah M and Adam MB 2009 On the outlier detection in nonlinear regression. 3(12), 243-250.

Riazoshams H and Midi H 2014 Robust Leverage and outlier detection measures in nonlienar regression, 2014 (Unpublished manuscript).

See Also

nl.fitt, nl.fitt.gn, nl.fitt, nl.fitt.gn, nl.fitt.rob, nl.fitt.rgn, nl.robmeas, nlr, nlout

Examples

d<-list(xr=Weights$Date, yr=Weights$Weight)
wmodel <- nlr(nlrobj1[[2]],data=d,control=nlr.control(method = "OLS",trace=TRUE))
a=nlout.JL(wmodel)
plot(a[[2]])

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