An object of class variogram contains empirical variogram estimates generated from a point object and a pair object. A
variogram object is stored as a data frame containing six columns: lags, bins, classic, robust, med, and n. The length of each
vector is equal to the number of lags in the pair object used to create the variogram object, say l. The lags vector contains the
lag numbers for each lag, beginning with one (1) and going to the number of lags (l). The bins vector contains the spatial midpoint
of each lag. The classic, robust, and med vectors contain the classical,
$$\gamma_{c}(h)=\frac{1}{n}\sum_{(i,j) \in
N(h)}(z(x_{i})-z(x_{j}))^{2}$$
robust, $$\gamma_{m}(h)=\frac{(\frac{1}{n}\sum_{(i,j) \in N(h)} (\sqrt{|z(x_{i})-z(x_{j})|}))^{4}}{0.457 + \frac{0.494}{n}}$$
and median
$$\gamma_{m}(h)=\frac{(\mbox{median}_{(i,j) \in N(h)}
(\sqrt{|z(x_{i})-z(x_{j})|}))^{4}}{0.457 + \frac{0.494}{|N(h)|}}$$
variogram estimates for each lag, respectively (see Cressie, 1993, p. 75).
The n vector contains the number $|N(h)|$ of pairs of points in each lag $N(h)$.
est.variogram(point.obj, pair.obj, a1, a2)point()pair()a1 and a2point, pair
maas.v<-est.variogram(maas.point,maas.pair,'zinc')
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