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

This function utilizes a regular grid of points to develop a sampling scheme or n polygons.

Usage

PointBasedSample(polygon, n = 20, reps = 100, BS.reps = 9999)

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.

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 
#>   0.620   0.002   0.623 
# 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$SummaryData)
#>                  Metric    Value
#> 1     variance.observed 16074215
#> 2        quantile.0.001 16084226
#> 3             lwr.95.CI 16074215
#> 4             upr.95.CI 16084384
#> 5    Voronoi.reps.asked       10
#> 6 Voronoi.reps.received        6
plot(out$Geometry)