Description Usage Arguments Value Examples

`nsCrosscorr`

calculates a nonstationary cross-correlation matrix
between two fixed sets of locations (a prediction set with M locations, and
the observed set with N locations), based on vectors of the unique anisotropy
parameters for each station. Since the correlation function uses a
spatially-varying Mahalanobis distance, this function requires coordinate-
specific distance matrices (see below). The function is coded as a
`nimbleFunction`

(see the `nimble`

package) but can also be
used as a regular R function.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
nsCrosscorr(
Xdist1_sq,
Xdist2_sq,
Xdist12,
Sigma11,
Sigma22,
Sigma12,
PSigma11,
PSigma22,
PSigma12,
nu,
d
)
``` |

`Xdist1_sq` |
M x N matrix; contains values of pairwise squared cross-distances in the x-coordinate. |

`Xdist2_sq` |
M x N matrix; contains values of pairwise squared cross-distances in the y-coordinate. |

`Xdist12` |
M x N matrix; contains values of pairwise signed cross/cross-
distances between the x- and y-coordinates. The sign of each element is
important; see |

`Sigma11` |
Vector of length N; contains the 1-1 element of the anisotropy process for each observed location. |

`Sigma22` |
Vector of length N; contains the 2-2 element of the anisotropy process for each observed location. |

`Sigma12` |
Vector of length N; contains the 1-2 element of the anisotropy process for each observed location. |

`PSigma11` |
Vector of length N; contains the 1-1 element of the anisotropy process for each prediction location. |

`PSigma22` |
Vector of length N; contains the 2-2 element of the anisotropy process for each prediction location. |

`PSigma12` |
Vector of length N; contains the 1-2 element of the anisotropy process for each prediction location. |

`nu` |
Scalar; Matern smoothness parameter. |

`d` |
Scalar; dimension of the spatial domain. |

A M x N cross-correlation matrix for two fixed sets of stations and fixed parameter values.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# Generate some coordinates and parameters
coords <- cbind(runif(100),runif(100))
Sigma11 <- rep(1, 100) # Identity anisotropy process
Sigma22 <- rep(1, 100)
Sigma12 <- rep(0, 100)
Pcoords <- cbind(runif(200),runif(200))
PSigma11 <- rep(1, 200) # Identity anisotropy process
PSigma22 <- rep(1, 200)
PSigma12 <- rep(0, 200)
nu <- 2
# Calculate distances
Xdist_list <- nsCrossdist(coords, Pcoords)
# Calculate the correlation matrix
XcorMat <- nsCrosscorr(Xdist_list$dist1_sq, Xdist_list$dist2_sq, Xdist_list$dist12,
Sigma11, Sigma22, Sigma12, PSigma11, PSigma22, PSigma12, nu, ncol(coords))
``` |

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