GWPR.light (version 0.2.1)

GWPR.moran.test: Moran's I Test for Panel Regression

Description

Moran's I test for spatial autocorrelation in residuals from an estimated panel linear model (plm).

Usage

GWPR.moran.test(plm_model, SDF, bw, adaptive = FALSE, p = 2,
                       kernel = "bisquare", longlat = FALSE, alternative = "greater")

Arguments

plm_model

An object of class inheriting from "plm", see plm

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

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

alternative

A character string specifying the alternative hypothesis, must be one of greater (default), less or two.sided.

Value

A list of result:

statistic

the value of the standard deviate of Moran's I.

p.value

the p-value of the test.

Estimated.I

the value of the observed Moran's I.

Excepted.I

the value of the expectation of Moran's I.

V2

the value of the variance of Moran's I.

alternative

a character string describing the alternative hypothesis.

References

Beenstock, M., Felsenstein, D., 2019. The econometric analysis of non-stationary spatial panel data. Springer.

Examples

# NOT RUN {
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

pdata <- plm::pdata.frame(TransAirPolCalif, index = c("GEOID", "year"))
moran.plm.model <- plm::plm(formula = formula.GWPR, data = pdata, model = "within")
summary(moran.plm.model)

#precomputed bandwidth
bw.AIC.Fix <- 2.010529

# moran's I test
GWPR.moran.test(moran.plm.model, SDF = California, bw = bw.AIC.Fix, kernel = "bisquare",
                 adaptive = FALSE, p = 2, longlat = FALSE, alternative = "greater")
# }