Some details fitting the variogram from the DEM data

# some simple least squares fits to the variogram to get an estimate of
# process variance (rho) and the nugget.  The nugget is essentially zero
# so using lambda=0 i.e. interpolation would be definsible

# only fit to variogram close to zero.

vgram.matrix( zR, R= 65, dx=dx, dy=dy) -> look

DD<- look$d.full

D2<- DD**2
Y<- look$vgram.full

lm( Y ~ D2)-> out0
lines( DD, predict( out0), col="red",lwd=1.5)
summary(out0) # little evidence for a nugget variance.

good<- DD <= 20 
DD<- DD[good]
Y<- look$vgram.full[good]

theta<- 40
X<- (1- Wendland(DD,k=2,theta=theta,dimension=2))
lm( Y ~ X-1)-> out
lines(DD, predict( out) , col="blue", lwd=2)

xt<- seq( 0,50,,150)

lines( xt, out$coefficient[1]*(1- Wendland(xt,k=2,theta=theta,dimension=2)),
             col="blue", lty=2)