compute_ppms: Computes the Predictive Performance Metrics for a...

Description Usage Arguments Value Examples

View source: R/ebirdst-ppms.R

Description

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_all_ppms() or plot_binary_by_time() to summarize the metrics.

Usage

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compute_ppms(path, ext, es_cutoff = 75)

Arguments

path

character; full path to directory containing the eBird Status and Trends products for a single species.

ext

ebirdst_extent object (optional); the spatiotemporal extent to filter the data to.

es_cutoff

integer between 0-100; the ensemble support cutoff to use in distinguishing zero and non-zero predictions.

Value

A list of three data frames: binary_ppms, occ_ppms, and 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_ppms contains 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 ebirdst_extent object.

Examples

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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)

ebirdst documentation built on Jan. 16, 2021, 5:16 p.m.