gwer.multiscale.diag: Diagnostic for Geographically Weighted Elliptical Regression...

In gwer: Geographically Weighted Elliptical Regression

Description

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

Usage

gwer.multiscale.diag(object, ...)

Arguments

object	an object with the result of the fitted multiscale geographically weighted 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

Brunsdon, C., Fotheringham, A. S. and Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298. doi: 10.1111/j.1538-4632.1996.tb00936.x

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

See Also

elliptical

Examples

data(georgia, package = "spgwr")
fit.formula <- PctBach ~ TotPop90 + PctRural + PctFB + PctPov
gwer.bw.t <- bw.gwer(fit.formula, data = gSRDF, family = Student(3), adapt = TRUE)
msgwr.fit.t <- gwer.multiscale(fit.formula, family = Student(3), data = gSRDF, 
                               bws0 = rep(gwer.bw.t, 5), hatmatrix = TRUE, 
                               adaptive = TRUE)
gwer.multiscale.diag(msgwr.fit.t)

gwer documentation built on April 28, 2021, 9:07 a.m.