Basic Methods

The covariance model class CovModel of GSTools provides a set of handy methods.

One of the following functions defines the main characterization of the variogram:

• CovModel.variogram : The variogram of the model given by

  $\gamma\left(r\right)= \sigma^2\cdot\left(1-\mathrm{cor}\left(s\cdot\frac{r}{\ell}\right)\right)+n$

• CovModel.covariance : The (auto-)covariance of the model given by

  $C\left(r\right)= \sigma^2\cdot\mathrm{cor}\left(s\cdot\frac{r}{\ell}\right)$

• CovModel.correlation : The (auto-)correlation (or normalized covariance) of the model given by

  $\rho\left(r\right) = \mathrm{cor}\left(s\cdot\frac{r}{\ell}\right)$

• CovModel.cor : The normalized correlation taking a normalized range given by:

  $\mathrm{cor}\left(h\right)$

As you can see, it is the easiest way to define a covariance model by giving a correlation function as demonstrated in the introductory example. If one of the above functions is given, the others will be determined: