sample_grid | R Documentation |
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.
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)
x |
data frame or sf object; the points to subsample. If |
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, |
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. |
Logical vector indicating which rows are selected.
## 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 100, 4 month grid s <- sample_grid(preds, res = 100000, t_res = 1 / 4) preds_grid <- preds[s, ] # case control sampling independently samples presence and absence s <- sample_case_control(preds, res = 1000000, 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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