Loads test data and ensemble support values and then calculates the
predictive performance metrics (PPMs) within a spatiotemporal extent defined by an
ebirdst_extent object. Use this function directly to access the computed
metrics, or use
plot_binary_by_time() to summarize the
compute_ppms(path, ext, es_cutoff = 75)
character; full path to directory containing the eBird Status and Trends products for a single species.
ebirdst_extent object (optional); the spatiotemporal extent to filter the data to.
integer between 0-100; the ensemble support cutoff to use in distinguishing zero and non-zero predictions.
A list of three data frames:
abd_ppms. These data frames have 25 rows corresponding to 25 Monte Carlo
iterations each estimating the PPMs using a spatiotemporal subsample of the
test data. Columns correspond to the different PPMS.
binary or range-based PPMS,
occ_ppms contains within-range occurrence
probability PPMS, and
abd_ppms contains within-range abundance PPMs. In
some cases, PPMs may be missing, either because there isn't a large enough
test set within the spatiotemporal extent or because average occurrence or
abundance is too low. In these cases, try increasing the size of the
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## Not run: # download and load example data sp_path <- ebirdst_download("example_data", tifs_only = FALSE) # define a spatiotemporal extent to plot bb_vec <- c(xmin = -86, xmax = -83, ymin = 42.5, ymax = 44.5) e <- ebirdst_extent(bb_vec, t = c("05-01", "05-31")) # compute predictive performance metrics ppms <- compute_ppms(path = sp_path, ext = e) ## End(Not run)
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