kriging.auto {klovan}

Automatic Kriging Interpolation with Factor Analysis Preprocessing

Description

This function performs automatic kriging interpolation with factor analysis preprocessing on input data. The optimization may not work as intended use higher num_init_test and num_fin_test values or run the function multiples times to ensure an accurate result.

Usage

kriging.auto(
  data,
  num_fac = 3,
  grid_cell_size = NA,
  num_init_test = 8,
  num_fin_test = 200,
  nugget_bounds = c(0, 0.2),
  sill_bounds = c(0, 20000),
  range_bounds = c(0, 25000)
)

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.

num_fac

A numeric value indicating the number of factors to analyze. Default is 3.

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.

num_init_test

The number of random starts for initial model optimization. Default is 8

num_fin_test

The number of random starts for final model optimization. Default is 200

nugget_bounds

A numeric vector specifying the lower and upper bounds for the nugget parameter during optimization. Default is c(0, .2).

sill_bounds

A numeric vector specifying the lower and upper bounds for the sill parameter during optimization. Default is c(0, 20000).

range_bounds

A numeric vector specifying the lower and upper bounds for the range parameter during optimization. Default is c(0, 25000).

Value

A data frame with interpolated data for the whole grid. Data frame has columns: "C_X", "C_Y", "value", "FA". "C_X" and "C_Y" are the coordinates, "value" is the interpolated value, and "FA" indicates the relevant factor the value corresponds to.

Examples

data("Klovan_Row80")
kriging.auto(Klovan_Row80)