This function thins species occurrence records in an n-dimensional environmental space by randomly sampling exactly one point from each occupied n-dimensional grid cell (hypercube).
Arguments
- data
A data.frame containing species occurrences and pre-scaled environmental variables, typically the output of `prepare_bean()`.
- env_vars
A character vector of two or more column names representing the environmental variables (dimensions) to use for thinning.
- grid_resolution
A numeric vector of resolutions for each environmental axis. Its length must match the length of `env_vars`.
- seed
(numeric) An optional random seed for reproducibility. If provided, the random number generator state is safely isolated to this function call and will not affect the global environment. Default = NULL.
Value
An object of class `bean_thinned`, which is a list containing:
- thinned_data
A data.frame containing the occurrence records that were retained after the thinning process.
- n_original
An integer representing the number of complete occurrence records in the input data before thinning.
- n_thinned
An integer representing the number of occurrence records remaining after thinning.
- parameters
A list of the key parameters used during the thinning process.
Examples
if (FALSE) { # \dontrun{
# Assume 'prepared_pca' is the output from prepare_bean(..., transform = "pca")
# and contains columns PC1, PC2, PC3.
# Thin the data in 3D environmental space, retaining 1 point per grid cell
thinned_3d <- thin_env_nd(
data = prepared_pca$prepared_data,
env_vars = c("PC1", "PC2", "PC3"),
grid_resolution = c(0.5, 0.5, 0.5), # One resolution value for each axis
seed = 123
)
# Print the summary
print(thinned_3d)
} # }