load_pis | R Documentation |
Loads the predictor importance (PI) data from the stixel_summary.db sqlite database. PI estimates are provided for each stixel over which a model was run and are identified by a unique stixel ID in addition to the coordinates of the stixel centroid.
load_pis(path, ext, model = c("occurrence", "count"), return_sf = FALSE)
path |
character; directory that the Status and Trends data for a given
species was downloaded to. This path is returned by |
ext |
ebirdst_extent object; the spatiotemporal extent to filter the data to. The spatial component of the extent object must be provided in unprojected, latitude-longitude coordinates. |
model |
character; whether to make estimates for the occurrence or count model. |
return_sf |
logical; whether to return an sf object of spatial points rather then the default data frame. |
Data frame, or sf object if return_sf = TRUE
, containing PI
estimates for each stixel for either the occurrence or count models. The
data are provided in a 'wide' format, with each row corresponding to the PI
estimates for a give stixel for the occurrence count model, and the
relative importance of each predictor in columns. Stixels are identified by
a unique stixel_id
, and the centroid of the stixel in space and time is
specified by the latitude
, longitude
, and day_of_year
columns. The
column predictor
provides a code specifying the predictor variable. These
codes can be looked up in ebirdst_predictors
for a brief description.
## 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") # load predictor importance for the occurrence model pis <- load_pis(path) # plot the top 15 predictor importances # define a spatiotemporal extent to plot data from bb_vec <- c(xmin = -86.6, xmax = -82.2, ymin = 41.5, ymax = 43.5) e <- ebirdst_extent(bb_vec, t = c("05-01", "05-31")) plot_pis(pis, ext = e, n_top_pred = 15, by_cover_class = TRUE) ## End(Not run)
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