Generate a sampling grid based off of regularly sampled points across the species range.

This function utilizes a regular, or nearly so in the case of existing collections, grid of points to develop a sampling scheme or n polygons.

Usage

PointBasedSample(polygon, n, collections, reps, BS.reps)

Arguments

polygon: the input sf polygon, i.e. species range or administrative unit, where sampling is desired.
n: Numeric. The total number of desired collections. Defaults to 20.
collections: an sf point geometry data set of where existing collections have been made.
reps: further arguments passed to np.boot
BS.reps: number of bootstrap replicates for evaluating results.

Value

A list containing two objects, the first the results of bootstrap simulations. The second an sf dataframe containing the polygons with the smallest amount of variance in size.

Examples

#' Utilize a grid based stratified sample for drawing up polygons
ri <- spData::us_states |>
  dplyr::select(NAME) |>
  dplyr::filter(NAME == 'Rhode Island') |>
  sf::st_transform(32617)
  
 system.time(
  out <- PointBasedSample(polygon = ri, reps = 10, BS.reps = 10) # set very low for example
 )
#>    user  system elapsed 
#>   0.880   0.004   0.884 
# the function is actually very fast; 150 voronoi reps, with 9999 BS should only take about
# 2 seconds per species so not much concern on the speed end of things!
head(out$SummaryData)
#>                  Metric    Value
#> 1     variance.observed 12358061
#> 2        quantile.0.001 12360106
#> 3             lwr.95.CI 12358061
#> 4             upr.95.CI 12573411
#> 5    Voronoi.reps.asked       10
#> 6 Voronoi.reps.received        7
plot(out$Geometry)