estimate_z_expansion: Estimate vertical expansion factor for 3D kriging

In afsc-gap-products/coldpool: AFSC/RACE Groundfish Assessment Program EBS and NBS temperature products

Estimate vertical expansion factor for 3D kriging

Description

Uses L-BFGS algorithm implemented in optim().

Usage

estimate_z_expansion(
  x,
  location_formula,
  kriging_formula,
  model,
  z_start,
  cv_index,
  anisotropy_parameters = c(0, 0, 0, 1, 1),
  vgm_width,
  nm = Inf,
  maxdist = Inf,
  use_for_mse = rep(TRUE, length(cv_index)),
  z_limits = c(10, 5e+05)
)

Arguments

x	data.frame containing
location_formula	Formula to use for location argument to gstat. See gstat documentation for description of the formula interface (see ?gstat::gstat).
kriging_formula	Formula to use for kriging. See gstat documentation for description of the formula interface (see ?gstat::gstat).
z_start	Starting value for vertical expansion estimation
cv_index	Index of folds for cross validation
vgm_width	Optional.
nm	Maximum number of nearest neighbor observations to use for interpolation.
use_for_mse	Logical vector indicating whether to use an observation to calculate MSE for cross-validation.
z_limits	Upper and lower bounds for z_expansion, for optimization.
anisotopy_parameters	Anisotropy parameters to use for ordinary kriging as a 5L vector. See: ?gstat::vgm. If NULL and estimate_anisotropy, anisotropy is estimated.
variogram_model	Character vector indicating which variogram model to use for interpolation. Valid options are exponential (exp), circular (cir), gaussian (gau), Bessel (bes), Matern (mat), or Stein's Matern (ste).

afsc-gap-products/coldpool documentation built on Feb. 25, 2024, 9:44 p.m.