conditionLatentObs: Assign conditioning sets for the SGV approximation
In BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models
Description
Usage
Arguments
Value
Description
conditionLatentObs assigns q_y(i) vs q_z(i) following Section 5.1
in Katzfuss and Guinness (2018). This function only needs to be run once
per SGV analysis.
Usage
1
conditionLatentObs(nID, coords_ord, N)
Arguments
nID
N x k matrix of neighbor indices.
coords_ord
N x 2 matrix of locations.
N
Scalar; number of locations (observed only!).
Value
A matrix indicating whether the conditioning set for each location
is on the latent process (y, 1) or the observed values (z, 0).
BayesNSGP documentation built on Jan. 9, 2022, 9:07 a.m.
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Description Usage Arguments Value
Description
conditionLatentObs assigns q_y(i) vs q_z(i) following Section 5.1
in Katzfuss and Guinness (2018). This function only needs to be run once
per SGV analysis.
Usage
1 | conditionLatentObs(nID, coords_ord, N)
|
Arguments
nID |
N x k matrix of neighbor indices. |
coords_ord |
N x 2 matrix of locations. |
N |
Scalar; number of locations (observed only!). |
Value
A matrix indicating whether the conditioning set for each location
is on the latent process (y, 1) or the observed values (z, 0).
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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