Partial dependence (PD) plots depict the relationship between the modeled occurrence probability and each of the predictor variables used in the model. Status and Trends provides the data to generate these plots for every stixel.
load_pds(path, ext, model = c("occurrence", "count"), return_sf = FALSE)character; directory that the Status and Trends data for a given
species was downloaded to. This path is returned by ebirdst_download()
or get_species_path().
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.
character; whether to make estimates for the occurrence or count model.
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 PD
estimates for each stixel for either the occurrence or count model. The
data frame will have the following columns:
stixel_id: unique stixel identifier
latitude and longitude: stixel centroid
day_of_year: center day of year for stixel
predictor: name of the predictor that the PD data correspond to, for a
full list of predictors consult the ebirdst_predictors data frame
predictor_value: value of the predictor variable at which PD is
evaluated
response: predicted response, occurrence or count, at the given value
of the predictor averaged across all the values of the other predictors
if (FALSE) {
# 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 partial dependence data for occurrence model
pds <- load_pds(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_pds(pds, "solar_noon_diff_mid", ext = e, n_bs = 5)
}