cond_sim: Conditional Simulation using Vecchia's approximation

In joeguinness/GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation

Conditional Simulation using Vecchia's approximation

Description

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.

Usage

cond_sim(
  fit = NULL,
  locs_pred,
  X_pred,
  y_obs = fit$y,
  locs_obs = fit$locs,
  X_obs = fit$X,
  beta = fit$betahat,
  covparms = fit$covparms,
  covfun_name = fit$covfun_name,
  m = 60,
  reorder = TRUE,
  st_scale = NULL,
  nsims = 1
)

Arguments

fit	GpGp_fit object, the result of fit_model
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 m gives better approximations.
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 spatial-temporal covariance function. When lonlat = TRUE, spatial scale is in radians (earth radius = 1).
nsims	Number of conditional simulations to return.

Details

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.

joeguinness/GpGp documentation built on Feb. 22, 2024, 9:43 a.m.