library("plm")
n = 100
sig=0*matrix(3,3)
sig[,1]=#(0.81,0.1,0.2)
sig[,2]=#(0.1,2.25,0.1)
sig[,3]=#(0.2,0.1,1)
x =normal(n,3)*sig
t =sort(uniform(n))
beta0=#(1.2, 1.3, 1.4) ; the true value
y =x*beta0+t^3+0.01*normal(n)
h =0.25
res=plmlorg(t,x,y, h,1)
res.hbeta ; the estimate of beta
ddp=createdisplay(1,1)
datah1=t~t^3
datah2=t~res.hg
part=grid(1,1,rows(t))'
setmaskp(datah1,1,0,1)
setmaskp(datah2,4,0,3)
setmaskl(datah1,part,1,1,1)
setmaskl(datah2,part,4,1,3)
show(ddp,1,1,datah1,datah2)
setgopt(ddp,1,1,"xlabel","T","title","Simulation comparison","ylabel","g(T) and its estimate values")