dot-cor_sep function

Calculate correlation for separable model

.cor_sep(spatial, temporal, par_s, par_t)

Arguments

• spatial: Pure spatial model, exp or cauchy for now.
• temporal: Pure temporal model, exp or cauchy for now.
• par_s: Parameters for the pure spatial model. Nugget effect supported.
• par_t: Parameters for the pure temporal model.

Returns

Correlations for separable model.

Details

The separable model is the product of a pure temporal model, $C_T(u)$, and a pure spatial model, $C_S(\mathbf{h})$. It is of the form

where $\delta_{x=0}$ is 1 when $x=0$ and 0 otherwise. Here $\mathbf{h}\in\mathbb{R}^2$ and $u\in\mathbb{R}$. Now only exponential and Cauchy correlation models are available.

References

Gneiting, T. (2002). Nonseparable, Stationary Covariance Functions for Space–Time Data, Journal of the American Statistical Association, 97:458, 590-600.