gwplm: This function implements basic geographically weighted panel...

In LAEQ/gwpr: Functions for calculating and mapping a geographically weighted panel regression (GWPR)

Description

This function implements basic geographically weighted panel regression (GWPR) The function requires the plm packages Je pense que ce package est utile seulement si l'on imprime les effets individuels/temporels (indisponible pour le moment)

Usage

gwplm(
  SpDF,
  data,
  index,
  formula,
  bw,
  kernel,
  adaptive = F,
  dMat,
  effect = c("individual", "time", "twoways", "nested"),
  model = c("within", "random", "ht", "between", "pooling", "fd")
)

Arguments

SpDF:	large SpatialPolygonsdataFrame
data:	dataFrame for the Panel data
index:	List for the indexes : (c(" ID, Time"))
formula:	Regression model formula : Y ~ X1 + ... + Xk
bw:	bandwidth to be used (see GWmodel::gwr.basic)
kernel:	gaussian,exponential, bisquare, tricube, boxcar (see GWmodel::gw.weight)
adaptive:	TRUE or FALSE (see GWmodel::gw.weight)
dMat:	a distance matrix or vector (Optional parameter, see GWmodel::gw.weight)
effect:	the effects introduced in the model, one of "individual", "time", or "twoways" (see plm::plm)
model:	one of "pooling", "within", "between", "random", "fd", or "ht" (see plm::plm)

Examples

data(USStates)
USStates@data$id <- c(1:length(unique(USStates@data[,"state"])))
data <- merge(USStates@data, Produc, by="state", all=TRUE)
dMat <- GWmodel::gw.dist(sp::coordinates(USStates), p=2, longlat=FALSE)
Equation <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp
bwCV.A <- bw.CV.A(formula=Equation, data=data, index=c("id","year"), effect='individual', model="within", kernel="bisquare", dMat=dMat, bws=c(30:40))
result <- gwplm(SpDF=USStates, data=data, index=c("id", "year"),formula=Equation, bw = bwCV.A, kernel="bisquare", adaptive=T, effect="individual", model="within", dMat=dMat)

LAEQ/gwpr documentation built on June 28, 2020, 8:23 p.m.