elliptical.diag: Diagnostic Measures for Elliptical Regression Models In gwer: Geographically Weighted Elliptical Regression

Description

This function obtains the values of different residuals types and calculates the diagnostic measures for the fitted elliptical regression model.

Usage

 `1` ```elliptical.diag(object, ...) ```

Arguments

 `object` an object with the result of the fitted elliptical regression model. `...` arguments to be used to form the default control argument if it is not supplied directly.

Value

Returns a list of diagnostic arrays:

 `ro` ordinal residuals. `rr` response residuals. `rp` pearson residuals. `rs` studentized residuals. `rd` deviance residuals. `dispersion` coefficient of dispersion parameter. `Hat` the hat matrix. `h` main diagonal of the hat matrix. `GL` generalized leverage. `GLbeta` generalized leverage of location parameters estimation. `GLphi` generalized leverage of dispersion parameters estimation. `DGbeta` cook distance of location parameters estimation. `DGphi` cook distance of dispersion parameters estimation. `Cic` normal curvature for case-weight perturbation. `Cih` normal curvature for scale perturbation. `Lmaxr` local influence on response (additive perturbation in responce). `Lmaxc` local influence on coefficients (additive perturbation in predictors).

References

Galea, M., Paula, G. A., and Cysneiros, F. J. A. (2005). On diagnostics in symmetrical nonlinear models. Statistics & Probability Letters, 73(4), 459-467. doi: 10.1016/j.spl.2005.04.033

`elliptical`
 ```1 2 3 4 5 6 7 8 9``` ```data(luzdat) y <- luzdat\$y x1 <- luzdat\$x1 ; x1 <- factor(x1) ; x1 <- C(x1, treatment) x2 <- luzdat\$x2 x3 <- (luzdat\$x2)^2 luz <- data.frame(y, x1, x2, x3) elliptical.fitt <- elliptical(y ~ x1+x2+x3, family = Student(df=5), data = luz) elliptical.diag(elliptical.fitt) ```