kriging {klovan}

Perform Kriging Interpolation

Description

This function performs kriging interpolation on spatial data using ridge regression to calculate the kriging weights. It uses either regular inverse or generalized inverse with ridge regression based on the availability of regular inverse for the given covariance matrix.

Usage

kriging(
  data,
  factor,
  grid_cell_size = NA,
  nugget,
  sill,
  range_val,
  a,
  model_name
)

Arguments

data

A dataset of class data.frame. The data should contain 'C_X' and 'C_Y' columns representing the x and y coordinates of the data points and excludes any rank, ID, or column not for analysis, see README for details

factor

The target factor (FAC) to be interpolated using kriging.

grid_cell_size

The desired cell size for the grid. Default is NA, which will calculate the cell size based on the average distance between data points.

nugget

The nugget effect parameter for the variogram model.

sill

The sill parameter for the variogram model.

range_val

The range parameter for the variogram model.

a

Additional parameter (depends on the variogram model) use NA if not needed.

model_name

The name of the model to use for variogram fitting and kriging. Options include "Sph1", "Exp1", "Gau1", "Mat1", "Pow1", "Quad1", "Card1", "Gam1", "Cau1", "Sta1", "Ord1", "Tri1", and "Cos1". use functionprint_model_names() for more information

Value

A data frame containing the interpolated values for the target factor (FAC).

Examples

data(Klovan_Row80)
# Perform kriging interpolation for FAC1
kriging_results <- kriging(Klovan_Row80, factor = 1, grid_cell_size = NA,
nugget=.0001, sill=2.5, range_val=1000, a=NA, model_name="Sph1")