sample_grid {ebirdst}

R Documentation

Spatiotemporal sampling of points on a grid

Description

Subsample points to deal with spatiotemporal bias in observations by defining a grid in space and time, then sampling the given number of points from each cell. sample_case_control() additionally samples presence and absence independently.

Usage

sample_grid(x, res, t_res, n = 1, replace = FALSE, jitter = TRUE)

sample_case_control(x, res, t_res, n = 1, replace = FALSE, jitter = TRUE)

Arguments

x	data frame or sf object; the points to subsample. If x is a data frame the coordiantes should be provided as columns lat and lon. The day of year should be expressed as a proportion from 0-1 and stored in the column date.
res	numeric; the size in meters of the grid to sample from. This can be a 2 element vector indicating the x and y dimensions of the cells.
t_res	numeric; the temporal resolution for sampling expressed as a proportion of the year. For example, 7 / 365 would result in sampling from each week.
n	integer; the number of points to sample from each grid cell.
replace	logical; whether to sample with replacement.
jitter	logical; to avoid always using the same grid for sampling, the grid can be jittered so that the origin is different each time this function is called.

Value

Logical vector indicating which rows are selected.

Examples

## Not run: 
# download example data
path <- ebirdst_download("example_data", tifs_only = FALSE)
# or get the path if you already have the data downloaded
path <- get_species_path("example_data")

# test data to sample
preds <- load_predictions(path, return_sf = TRUE)

# sample on a 100km, 1 month grid
s <- sample_grid(preds, res = 100000, t_res = 1 / 12)
preds_grid <- preds[s, ]

# case control sampling independently samples presence and absence
s <- sample_case_control(preds, res = 100000, t_res = 1 / 12)
preds_cc <- preds[s, ]

# grid sampling preserves the presence/absence ratio
table(preds$obs > 0) / nrow(preds)
table(preds_grid$obs > 0) / nrow(preds_grid)
# while case control sampling increases the prevelance of presences
table(preds_cc$obs > 0) / nrow(preds_cc)

# plot
library(sf)
p <- par(mar = c(0, 0, 0, 0))
plot(st_geometry(preds), col = "black", pch = 19, cex = 0.2)
plot(st_geometry(preds_cc), col = "red", pch = 19, cex = 0.5, add = TRUE)

## End(Not run)

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