dot-cor_sep: Calculate correlation for separable model
In mcgf: Markov Chain Gaussian Fields Simulation and Parameter Estimation

.cor_sep

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, `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.

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.

mcgf documentation built on June 29, 2024, 9:09 a.m.