bw.GWPR: Bandwidth selection for basic GWPR In GWPR.light: Geographically Weighted Panel Regression

Description

A function for automatic bandwidth selection to calibrate a GWPR model

Usage

 1 2 3 4 5 6 bw.GWPR(formula, data, index, SDF, adaptive = FALSE, p = 2, bigdata = FALSE, upperratio = 0.25, effect = "individual", model = c("pooling", "within", "random"), random.method = "swar", approach = c("CV","AIC"), kernel = "bisquare", longlat = FALSE, doParallel = FALSE, cluster.number = 2, human.set.range = FALSE, h.upper = NULL, h.lower = NULL)

Arguments

 formula The regression formula: : Y ~ X1 + ... + Xk data 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 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 bigdata TRUE or FALSE, if the dataset exceeds 40,000, we strongly recommend set it TRUE upperratio Set the ratio between upper boundary of potential bandwidth range and the forthest distance of SDF, if bigdata = T. (default value: 0.25) effect The effects introduced in the model, one of "individual" (default) , "time", "twoways", or "nested" model Panel model transformation: (c("within", "random", "pooling")) random.method Method of estimation for the variance components in the random effects model, one of "swar" (default), "amemiya", "walhus", or "nerlove" approach Score used to optimize the bandwidth, c("CV", "AIC") 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 doParallel If TRUE, "cluster": multi-process technique with the parallel package would be used. cluster.number The number of the clusters that user wants to use human.set.range If TRUE, the range of bandwidth selection could be set by the user h.upper The upper boundary of the potential bandwidth range. h.lower The lower boundary of the potential bandwidth range.

Value

The optimal bandwidth

Author(s)

Chao Li <chaoli0394@gmail.com> Shunsuke Managi

References

Fotheringham, A. Stewart, Chris Brunsdon, and Martin Charlton. Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons, 2003.

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 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 bw.CV.Fix <- bw.GWPR(formula = formula.GWPR, data = TransAirPolCalif, index = c("GEOID", "year"), SDF = California, adaptive = FALSE, p = 2, bigdata = FALSE, effect = "individual", model = "within", approach = "CV", kernel = "bisquare", longlat = FALSE) bw.CV.Fix bw.AIC.Fix <- bw.GWPR(formula = formula.GWPR, data = TransAirPolCalif, index = c("GEOID", "year"), SDF = California, adaptive = FALSE, p = 2, bigdata = FALSE, effect = "individual", model = "within", approach = "AIC", kernel = "bisquare", longlat = FALSE, doParallel = FALSE) bw.AIC.Fix

GWPR.light documentation built on Oct. 18, 2021, 5:09 p.m.