GWPR.phtest | R Documentation |
Locally Hausman Test Based on GWPR
GWPR.phtest(formula, data, index, SDF, bw = NULL, adaptive = FALSE, p = 2, effect = "individual", random.method = "swar", kernel = "bisquare", longlat = FALSE)
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 |
A list of result:
a list class object including the model fitting parameters for generating the report file
a Spatial*DataFrame (either Points or Polygons, see sp) integrated with fit.points, test value, p value, df
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.
Chao Li <chaoli0394@gmail.com> Shunsuke Managi
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))
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