Automatically Fit a Variogram Model

This function was originally sourced from the `automap` package (version 1.1-16), which is no longer available on CRAN. It is provided here with minimal modification for internal use in this package.

`autofitVariogram()` automates the fitting of a variogram model to spatial input data by testing a range of model types and kappa values (for Matern and Stein models), and selecting the best-fit model based on the sum of squared errors.

Dependencies from `gstat`, `sp`, and `sf` are required for this function to operate correctly.

Usage

autofitVariogram(
  formula,
  input_data,
  model = c("Sph", "Exp", "Gau", "Ste"),
  kappa = c(0.05, seq(0.2, 2, 0.1), 5, 10),
  fix.values = c(NA, NA, NA),
  verbose = FALSE,
  GLS.model = NA,
  start_vals = c(NA, NA, NA),
  miscFitOptions = list(),
  ...
)

Arguments

formula

model formula for the variogram (e.g., `z ~ 1`)

input_data

an `sf` or `Spatial*` object containing the spatial data

model

a character vector of variogram model names (e.g., `"Sph"`, `"Exp"`)

kappa

vector of kappa values for Matern/Stein models

fix.values

optional vector of fixed values for nugget, range, sill

verbose

logical; if `TRUE`, prints additional output

GLS.model

optional variogram model for Generalized Least Squares

start_vals

optional vector of starting values for nugget, range, sill

miscFitOptions

named list of additional control options

...

additional arguments passed to `gstat::variogram()`

Value

a list with class `"autofitVariogram"` containing:

exp_var

the empirical variogram

var_model

the best-fit variogram model object

sserr

sum of squared errors for the best model

Note

Original author: J. H. Hiemstra (2013), `automap` package. License: GPL-2 | GPL-3.

Examples

if (FALSE) { # \dontrun{
result <- autofitVariogram(z ~ 1, input_data)
plot(result$exp_var)
plot(result$var_model)
} # }