areaOfEffect (version 0.2.4)

aoe_sample: Stratified Sampling from AoE Results

Description

Sample points from an aoe_result with control over core/halo balance. This is useful when core regions dominate due to point density, and you want balanced representation for modelling.

Usage

aoe_sample(x, ...)
# S3 method for default
aoe_sample(x, ...)
# S3 method for aoe_result
aoe_sample(
  x,
  n = NULL,
  ratio = c(core = 0.5, halo = 0.5),
  replace = FALSE,
  by = c("overall", "support"),
  ...
)

Value

An aoe_result object containing the sampled points, preserving all original columns and attributes. Has additional attribute sample_info with details about the sampling.

Arguments

x

An aoe_result object returned by aoe() or aoe_expand().

...

Additional arguments passed to methods.

n

Total number of points to sample. If NULL, uses all available points subject to the ratio constraint (i.e., downsamples the larger group).

ratio

Named numeric vector specifying the target proportion of core and halo points. Must sum to 1. Default is c(core = 0.5, halo = 0.5) for equal representation.

replace

Logical. Sample with replacement? Default is FALSE. If FALSE and n exceeds available points in a stratum, that stratum contributes all its points.

by

Character. Stratification grouping:

• "overall" (default): sample from all points regardless of support

• "support": apply ratio within each support separately

Details

Sampling modes

Fixed n: When n is specified, the function samples exactly n points (or fewer if not enough available), distributed according to ratio.

Balanced downsampling: When n is NULL, the function downsamples the larger stratum to match the smaller one according to ratio. For example, with ratio c(core = 0.5, halo = 0.5) and 100 core + 20 halo points, it returns 20 core + 20 halo = 40 points.

Multiple supports

With by = "support", sampling is done independently within each support, then results are combined. This ensures each support contributes balanced samples. With by = "overall", all points are pooled first.

See Also

aoe() for computing AoE classifications

Examples

library(sf)

support <- st_as_sf(
  data.frame(id = 1),
  geometry = st_sfc(st_polygon(list(
    cbind(c(0, 100, 100, 0, 0), c(0, 0, 100, 100, 0))
  ))),
  crs = 32631
)

# Many points in core, few in halo
set.seed(42)
pts <- st_as_sf(
  data.frame(id = 1:60),
  geometry = st_sfc(c(
    lapply(1:50, function(i) st_point(c(runif(1, 10, 90), runif(1, 10, 90)))),
    lapply(1:10, function(i) st_point(c(runif(1, 110, 140), runif(1, 10, 90))))
  )),
  crs = 32631
)

result <- aoe(pts, support, scale = 1)

# Balance core/halo (downsamples core to match halo)
balanced <- aoe_sample(result)

# Fixed sample size with 70/30 split
sampled <- aoe_sample(result, n = 20, ratio = c(core = 0.7, halo = 0.3))