function for the best kappa parameter — gos

Computationally optimized function for determining the best kappa parameter for the optimal similarity

Usage

gos_bestkappa(formula,data = NULL,kappa=seq(0.05,1,0.05),
              nrepeat = 10,nsplit = 0.5,cores = 1)

Arguments

formula: A formula of GOS model
data: A data.frame or tible of observation data
kappa: (optional)A numeric vector of the optional percentages of observation locations with high similarity to a prediction location. kappa = 1 - tau, where tau is the probability parameter in quantile operator. kappa = 0.25 means that 25% of observations with high similarity to a prediction location are used for modelling.
nrepeat: (optional)A numeric value of the number of cross-validation training times. The default value is 10.
nsplit: (optional)The sample training set segmentation ratio,which in (0,1), default is 0.5.
cores: positive integer(default is 1). If cores > 1, a 'parallel' package cluster with that many cores is created and used. You can also supply a cluster object.

Value

A list of the result of the best kappa and the computation process curve.

References

Song, Y. (2022). Geographically Optimal Similarity. Mathematical Geosciences. doi: 10.1007/s11004-022-10036-8.

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

if (FALSE) {
library(dplyr)
library(ggplot2)
library(ggrepel)
data(zn)
data(grid)
system.time({
  b1 = gos_bestkappa(Zn ~ Slope + Water + NDVI  + SOC + pH + Road + Mine,
                     data = zn,kappa = c(0.01, 0.05, 0.1, 0.2, 0.5, 1),
                     nrepeat = 2,cores = 1)
})
b1$bestkappa
b1$plot
}