GWPR.phtest: Locally Hausman Test Based on GWPR

In GWPR.light: Geographically Weighted Panel Regression

Locally Hausman Test Based on GWPR

Description

Locally Hausman Test Based on GWPR

Usage

GWPR.phtest(formula, data, index, SDF, bw = NULL, adaptive = FALSE,
                   p = 2, effect = "individual", random.method = "swar",
                   kernel = "bisquare", longlat = FALSE)

Arguments

formula	The regression formula: : Y ~ X1 + ... + Xk
data	A data.frame for the Panel data.
index	A vector of the two indexes: (c("ID", "Time")).
SDF	Spatial*DataFrame on which is based the data, with the "ID" in the index.
bw	The optimal bandwidth, either adaptive or fixed distance.
adaptive	If TRUE, adaptive distance bandwidth is used, otherwise, fixed distance bandwidth.
p	The power of the Minkowski distance, default is 2, i.e. the Euclidean distance
effect	The effects introduced in the fixed effects model, one of "individual" (default) , "time", "twoways"
random.method	Method of estimation for the variance components in the random effects model, one of "swar" (default), "amemiya", "walhus", or "nerlove"
kernel	bisquare: wgt = (1-(vdist/bw)^2)^2 if vdist < bw, wgt=0 otherwise (default); gaussian: wgt = exp(-.5*(vdist/bw)^2); exponential: wgt = exp(-vdist/bw); tricube: wgt = (1-(vdist/bw)^3)^3 if vdist < bw, wgt=0 otherwise; boxcar: wgt=1 if dist < bw, wgt=0 otherwise
longlat	If TRUE, great circle distances will be calculated

Value

A list of result:

GW.arguments

a list class object including the model fitting parameters for generating the report file

SDF

a Spatial*DataFrame (either Points or Polygons, see sp) integrated with fit.points, test value, p value, df

Note

If the random method is "swar", to perform this test, bandwidth selection must guarantee that enough individuals in the subsamples. Using bw.GWPR function can avoid mistake.

Author(s)

Chao Li <[e-mail]> Shunsuke Managi

Examples

data(TransAirPolCalif)
data(California)
formula.GWPR <- pm25 ~ co2_mean + Developed_Open_Space_perc + Developed_Low_Intensity_perc +
   Developed_Medium_Intensity_perc + Developed_High_Intensity_perc +
   Open_Water_perc + Woody_Wetlands_perc + Emergent_Herbaceous_Wetlands_perc +
   Deciduous_Forest_perc + Evergreen_Forest_perc + Mixed_Forest_perc +
   Shrub_perc + Grassland_perc + Pasture_perc + Cultivated_Crops_perc +
   pop_density + summer_tmmx + winter_tmmx + summer_rmax + winter_rmax

#precomputed bandwidth
bw.AIC.Fix <- 7.508404

GWPR.phtest.resu.F <- GWPR.phtest(formula = formula.GWPR, data = TransAirPolCalif,
                                  index = c("GEOID", "year"), SDF = California,
                                  bw = bw.AIC.Fix, adaptive = FALSE, p = 2,
                                  effect = "individual", kernel = "bisquare",
                                  longlat = FALSE)
library(tmap)
tm_shape(GWPR.phtest.resu.F$SDF) +
     tm_polygons(col = "p.value", breaks = c(0, 0.05, 1))

GWPR.light documentation built on June 21, 2022, 5:05 p.m.