nlr (version 0.1-3)

nlout: Nonlinear outlier detection.

Description

Detecting outlier for nonlinear regression, is based on mixing robust estimates and statsitics measures.

Usage

nlout(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.

Value

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

"vmat"

variance covariance matrix of parameters $$\sigma^2 (\nabla f(\theta)'\nabla f(\theta))^{-1})$$

"d.yhat"

predicted values after rremoving a point \(\hat y_{(-i)}\)

"studres"

nl.robmeas object Studentized residuals.

"cook"

nl.robmeas object od Elliptic Norm (Cook Dist)

"mahd.v"

nl.robmeas object of Regression Mahalanobis Distance.

"mahd.dt"

nl.robmeas object of Mahalanobis MVE, data.

"mahd.xs"

nl.robmeas object of Mahalanobis MVE, xs.

"hadi"

nl.robmeas object of Hadi potential.

"potmah"

nl.robmeas object of Potential mahalanobis.

"delstud"

nl.robmeas object of Deletion Studentized.

"dffits"

nl.robmeas object of DFFITS.

"atk"

nl.robmeas object of Atkinson Distance.

"mvedta"

nl.robmeas object of MVE data.

"mvex"

nl.robmeas object of MVE x.

"dfbetas"

nl.robmeas object of DFBETAS.

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.

References

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

See Also

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

Examples

# NOT RUN {
 d<-list(xr=Weights$Date, yr=Weights$Weight)
 wmodel <- nlr(nlrobj1[[2]],data=d,control=nlr.control(method = "OLS",trace=TRUE))
 a=nlout(wmodel)
 ## Run the command as bellow
 ## nlout(wmodel)

# }