gwer (version 2.1)

summary.gwer: Summarizing Geographically Weighted Elliptical Regression Model Fits.

Description

This function produce result summary of the result of the fitted geographically weighted elliptical regression model.

Usage

# S3 method for gwer
summary(object, ...)

Arguments

object

an object with the result of the fitted geographically weighted elliptical regression model.

...

arguments to be used to form the default control argument if it is not supplied directly.

Value

returns an object of class “summary.gwer”, a list with follow components:

coefficients

the matrix of summarizing coefficients, standard errors and significance values for parameters hypothesis test.

dispersion

either the supplied argument or the estimated dispersion with standard error.

residuals

the residuals from object.

family

family from object.

results

a list of results values for fitted geographically weighted elliptical model.

spdisp

a logical value indicating whether the dispersion varies geographically from object.

df

degrees of fredom from object.

terms

the terms object used.

inter

number of iterations of optimization process.

nas

a logical vector indicating if there is na in estimation of coefficients.

type

a character string indicating the type of residuals was obtained from object

hatmatrix

a logical value indicating if hatmatrix was obtained from object

call

the matched call from object.

scale

values of the 4d_g for the specified distribution from object.

scaledispersion

values of the 4f_g for the specified distribution from object.

scalevariance

values of the scale variance for the specified distribution from object.

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. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x

Cysneiros, F. J. A., Paula, G. A., and Galea, M. (2007). Heteroscedastic symmetrical linear models. Statistics & probability letters, 77(11), 1084-1090. https://doi.org/10.1016/j.spl.2007.01.012

See Also

gwer, gwer.sel, family.elliptical

Examples

# NOT RUN {
data(columbus, package="spData")
fit.lm <- lm(CRIME ~ INC, data=columbus)
summary(fit.lm)
gwer.bw <- gwer.sel(CRIME ~ INC, data=columbus, family = Normal(),
                 coords=cbind(columbus$X, columbus$Y))
fit.gwer <- gwer(CRIME ~ INC, family = Normal(), bandwidth = gwer.bw, hatmatrix = TRUE,
                 spdisp = TRUE, parplot = TRUE, data=columbus, method = "gwer.fit",
                 coords=cbind(columbus$X, columbus$Y))
summary(fit.gwer)  
# }
# NOT RUN {
data(columbus, package="spData")
fit.elliptical <- elliptical(CRIME ~ INC, family = Student(df=4), data=columbus)
summary(fit.elliptical)
gwer.bw <- gwer.sel(CRIME ~ INC, data=columbus, family = Student(df=4),
                 coords=cbind(columbus$X, columbus$Y), method = 'aic')
gwer.fitt <- gwer(CRIME ~ INC, family = Student(df=4), bandwidth = gwer.bw, hatmatrix = TRUE,
                 spdisp = TRUE, parplot = TRUE, data=columbus, method = "gwer.fit",
                 coords=cbind(columbus$X, columbus$Y))
summary(gwer.fitt)  
# }