gwer.multiscale.diag.plots: Diagnostic Plots for Multiscale Geographically Weighted...

In gwer: Geographically Weighted Elliptical Regression

Description

This function generate diagnostic measures plots for the fitted multiscale geographically weighted elliptical regression models.

Usage

gwer.multiscale.diag.plots(
  object,
  mgwerdiag = NULL,
  which,
  subset = NULL,
  iden = F,
  labels = NULL,
  ret = F,
  ...
)

Arguments

object	an object with the result of the fitted multiscale geographically weighted elliptical regression models.
mgwerdiag	object list containing the diagnostic measures. By default it is obtained from the object, but can be calculated using gwer.multiscale.diag.
which	an optional numeric value with the number of only plot that must be returned.
subset	an optional numeric vector specifying a subset of observations to be used in the fitting process.
iden	a logical value used to identify observations. If TRUE the observations are identified by user in the graphic window.
labels	a optional string vector specifying a labels plots.
ret	a logical value used to return the diagnostic measures computing. If TRUE the diagnostic measures are returned (see gwer.multiscale.diag for more details).
...	graphics parameters to be passed to the plotting routines.

Value

Return an interactive menu with eleven options to make plots. This menu contains the follows graphics: 1: plot: All. 2: plot: Response residual against fitted values. 3: plot: Response residual against index. 4: plot: Quantile residual against fitted values. 5: plot: Quantile residual against index. 6: plot: QQ-plot of response residuals. 7: plot: QQ-plot of Quantile residuals. 8: plot: Generalized leverage. 9: plot: Total local influence index plot for response perturbation. 10: plot: Total local influence index plot scale perturbation. 11: plot: Total local influence index plot case-weight perturbation.

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

gwer.multiscale, gwer.multiscale.diag

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.plots(msgwr.fit.t, which=3)

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