Awsreg {SpatialRegimes}

Spatial clusterwise regression by an iterated spatially weighted regression algorithm

Description

This function implements a spatial clusterwise regression based on the procedure suggested by Andreano et al. (2017) and Bille' et al. (2017).

Usage

Awsreg(data, coly,colx,kernel,kernel2,coords,bw,tau,niter,conv,eta,numout,sout)

Arguments

data

A data.frame.

coly

The dependent variable in the c("y_ols") form.

colx

The covariates in the c("x1","x2") form.

kernel

Kernel function used to calculate distances between units (default is "bisquare", other values: "exponential", "gaussian","tricube").

kernel2

Kernel function used to calculate distances between units in the second step (default is "gaussian", other values: "exponential").

coords

The coordinates in terms of longitude and latitude.

bw

The bandwidth parameter of the initial weights.

tau

The confidence test parameter of the difference between regression parameters.

niter

The maximum number of iterations.

conv

The smallest accepted difference between the weights in two successive iterations.

eta

The parameter that regulates which is the weight of the weights of the previous iteration in the moving average that updates the new weights.

numout

The minimum number of areal units accepted for each cluster.

sout

Minimum value of weights such as to be considered equal to zero. Parameter used essentially to control clusters consisting of too few areal units.

Details

Author really thanks Bille' A.G. for her contribution to revising the original code.

Value

A object of Awsreg class with:

groups

Estimated clusters.

Author(s)

R. Benedetti

References

Andreano, M.S., Benedetti, R., and Postiglione, P. (2017). "Spatial regimes in regional European growth: an iterated spatially weighted regression approach", Quality & Quantity. 51, 6, 2665-2684.

Bille', A.G., Benedetti, R., and Postiglione, P. (2017). "A two-step approach to account for unobserved spatial heterogeneity", Spatial Economic Analysis, 12, 4, 452-471.

Examples

data(SimData)
SimData = SimData[1:50,]
coords = cbind(SimData$long, SimData$lat)

#######################

dmat<-gw.dist(coords,focus=0,p=2,theta=0,longlat=FALSE)
bw<-bw.gwr(y_ols~A+L+K,
           data=SpatialPointsDataFrame(coords,SimData),
           approach="AIC",kernel="bisquare",
           adaptive=TRUE,p=2,theta=0,longlat=FALSE,dMat=dmat)

#######################

aws<-Awsreg(data=SimData,
           coly=c("y_ols"),
           colx=c("A","L","K"),
           kernel="bisquare",
           kernel2="gaussian",
           coords=coords,
           bw=bw,
           tau=0.001,
           niter=200,
           conv=0.001,
           eta=0.5,
           numout=15,
           sout=1e-05)

SimData$regimes = aws$groups
plot(lat~long,SimData,col=regimes,pch=16)