plot_pis | R Documentation |
For a given eBird Status and Trends species, produce a box plot showing the predictor importance (PI) for each of the predictors used in the occurrence model. Predictors are plotted in order from highest to lowest importance. Many function parameters allow for customized plots.
plot_pis( pis, ext, by_cover_class = TRUE, n_top_pred = 15, pretty_names = TRUE, plot = TRUE )
pis |
data frame; predictor importance data from |
ext |
ebirdst_extent object; the spatiotemporal extent over which to calculate PIs. This is required, since results are less meaningful over large spatiotemporal extents. |
by_cover_class |
logical; whether to aggregate the FRAGSTATS metrics (PLAND and ED) for the land cover classes into single values for the land cover classes. |
n_top_pred |
integer; how many predictors to show. |
pretty_names |
logical; whether to convert cryptic land cover codes to readable land cover class names. |
plot |
logical; whether to plot predictor importance or just return top predictors. |
Plots a boxplot of predictor importance and invisibly returns the PI
data subset to just the top predictors, grouped and renamed according to
by_cover_class
and pretty_names
.
## 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 pis <- load_pis(path) # define a spatiotemporal extent to plot data from bb_vec <- c(xmin = -86, xmax = -83, ymin = 41.5, ymax = 43.5) e <- ebirdst_extent(bb_vec, t = c("05-01", "05-31")) top_pred <- plot_pis(pis, ext = e, n_top_pred = 10) top_pred ## End(Not run)
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