Predict an mPP Gaussian-process random effect at a new location
Source:R/spatial_interpolation.R
predict_one_gp_mpp.RdSingle-GP version. Used internally by predict_spatial_dual_gp() for
each of the two (intercept, slope) fields. Matches the Matern 3/2 kernel
and the chordal (3-D Euclidean on the sphere, km) metric of the Stan model.
Usage
predict_one_gp_mpp(
coords_new,
knot_coords,
z_knots,
sigma_draws,
ls_km_draws,
metric,
scaling = NULL,
jitter = 1e-04
)Arguments
- coords_new
matrix(n_obs, 2) of (lon, lat) in DEGREES.
- knot_coords
matrix(n_knots, 2) of (lon, lat) in DEGREES.
- z_knots
matrix(n_draws, n_knots) of standardized knot effects (e.g.
z_intercept_spatial[1..125]from the posterior).- sigma_draws
numeric(n_draws), the GP marginal SD.
- ls_km_draws
numeric(n_draws), the GP length scale in km (e.g.
ls_intercept_km).- metric
character; the metric the posterior was FITTED under, one of "chordal" (3-D km; length scale used directly) or "standardized" (former per-axis standardized coords; requires
scaling). A posterior must be predicted with the same metric it was fitted under, or the interpolation is silently wrong.- scaling
list with
lon_mean,lon_sd,lat_mean,lat_sd; required only when metric == "standardized".- jitter
ridge added to K_knots for numerical stability.