.cor_sep {mcgf} | R Documentation |
Calculate correlation for separable model
Description
Calculate correlation for separable model
Usage
.cor_sep(spatial, temporal, par_s, par_t)
Arguments
spatial |
Pure spatial model, |
temporal |
Pure temporal model, |
par_s |
Parameters for the pure spatial model. Nugget effect supported. |
par_t |
Parameters for the pure temporal 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
C(\mathbf{h}, u)=C_{T}(u)
\left[(1-\text{nugget})C_{S}(\mathbf{h})+\text{nugget}
\delta_{\mathbf{h}=0}\right],
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.
Value
Correlations for separable model.
References
Gneiting, T. (2002). Nonseparable, Stationary Covariance Functions for Space–Time Data, Journal of the American Statistical Association, 97:458, 590-600.