Sample Existing Data Using Balanced Sampling — sample_existing

This function samples a given set of existing data using balanced sampling techniques, which ensures that each stratum or subgroup of data is proportionally represented in the sample.

Usage

sample_existing_balanced(
  existing,
  nSamp,
  algorithm = "lpm2_kdtree",
  p = NULL,
  filename = NULL,
  overwrite = FALSE,
  ...
)

Arguments

existing: sf 'POINT'. Existing plot network.
nSamp: Numeric. Number of desired samples.
algorithm: Character. One of lpm2_kdtree, lcube, lcubestratified.
p: Numeric. Vector with length equal to the number of cells in mraster representing the inclusion probability for each candidate sample. Default = nSamp / N, where N is the number of cells.
filename: Character. Path to write stratified raster to disc.
overwrite: Logical. Specify whether filename should be overwritten on disc.
...: Additional arguments to pass to the selected sampling algorithm. This is leveraged when used by sample_existing() internally

Value

An sf object that is a sub-sample of existing