kriging.auto {klovan} | R Documentation |
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.
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)
)
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). |
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.
data("Klovan_Row80")
kriging.auto(Klovan_Row80)