### local kernel estimator 拒不线性核估计
LKE <- function(m,n,h,
data,
SearchPoints0,
kernel='bivariate kernal',
xmin=-1000,
xmax=1000,
ymin=-1000,
ymax=1000,
scale,
Dist,
index){
data <- as.data.table(data)
# Area <- SearchPoints(m,n,SearchPoints0,xmin,xmax,ymin,ymax)#寻找邻域内的点
# Area <-merge(Area,data,by=c("x","y"))#从输入数据中匹配出邻域内的点的各值
Area <- data[Dist[,index]<=h]
#if(kernel=='bivariate kernal'){
Area$x <- Area$x-m
Area$y <- Area$y-n
Area$K <- BivKernal(x=Area[['x']]/scale,y=Area[['y']]/scale,h=h/scale)#计算核的值,权重
#}
Area <- as.data.frame(Area)
beta <- WLS(Area[,-ncol(Area)],W=diag(Area$K))#加权最小二乘法
return(beta)
}
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