Description Usage Arguments Details
With the prediction locations ordered after the observation locations, an approximation for the inverse Cholesky of the covariance matrix is computed, and standard formulas are applied to obtain a conditional simulation.
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fit 
GpGp_fit object, the result of 
locs_pred 
prediction locations 
X_pred 
Design matrix for predictions 
y_obs 
Observations associated with locs_obs 
locs_obs 
observation locations 
X_obs 
Design matrix for observations 
beta 
Linear mean parameters 
covparms 
Covariance parameters 
covfun_name 
Name of covariance function 
m 
Number of nearest neighbors to use. Larger 
reorder 
TRUE/FALSE for whether reordering should be done. This should generally be kept at TRUE, unless testing out the effect of reordering. 
st_scale 
amount by which to scale the spatial and temporal
dimensions for the purpose of selecting neighbors. We recommend setting
this manually when using a spatialtemporal covariance function. When

nsims 
Number of conditional simulations to return. 
We can specify either a GpGp_fit object (the result of
fit_model
), OR manually enter the covariance function and
parameters, the observations, observation locations, and design matrix. We
must specify the prediction locations and the prediction design matrix.
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