auto_krige_model_CV_haversine: Fitting multiple variogram models using haversine distance...

In Martins6/distgeomodel: Kriging with Non-Euclidean Distances

Description

Fitting multiple variogram models using haversine distance (default) or euclidean.

Usage

auto_krige_model_CV_haversine(
  df,
  distance = "haversine",
  best_model_criteria = "MSE",
  max_dist,
  model_classes = c("matern", "exponential", "gaussian", "spherical"),
  weights_types = c("npairs", "cressie", "equal")
)

Arguments

df	A dataframe that has 'value' and two coordinates columns 'lat', 'long' for haversine or 'x' and 'y' for euclidean.
distance	Type of distance to be choosen.
best_model_criteria	The criteria to be taken for the choosing of the best model.
max_dist	Maximum distance to be considered in the fitting of variogram models.
model_classes	Classes of variogram models to be considered.
weight_types	Weights considered in the fitting of the variogram models.

Value

A list containing all the results of the combinations of the model and a gstat object of the best model.

Martins6/distgeomodel documentation built on Nov. 12, 2020, 5:35 p.m.