This function creates different plots depending on the input.
Usage
plot_occurrences(i, spp_name = NULL, pa = TRUE)
plot_grid(i)
plot_predictors(i, variables_selected = NULL)
plot_scenarios(i, variables_selected = NULL, scenario = NULL)
plot_predictions(
i,
spp_name = NULL,
scenario = NULL,
id = NULL,
ensemble = TRUE,
ensemble_type = "mean_occ_prob"
)
mapview_grid(i)
mapview_occurrences(i, spp_name = NULL, pa = TRUE)
mapview_predictors(i, variables_selected = NULL)
mapview_scenarios(i, variables_selected = NULL, scenario = NULL)
mapview_predictions(
i,
spp_name = NULL,
scenario = NULL,
id = NULL,
ensemble = TRUE,
ensemble_type = "mean_occ_prob"
)
Arguments
- i
Object to be plotted. Can be a
input_sdm
, but alsooccurrences
orsdm_area
.- spp_name
A character with species to be plotted. If NULL, the first species is plotted.
- pa
Boolean. Should pseudoabsences be plotted together? (not implemented yet.)
- variables_selected
A character vector with names of variables to be plotted.
- scenario
description
- id
The id of models to be plotted (only used when
ensemble = FALSE
). Possible values are row names of get_validation_metrics(i).- ensemble
Boolean. Should the ensemble be plotted (TRUE)? Otherwise a prediction will be plotted
- ensemble_type
Character of the type of ensemble to be plotted. One of: "mean_occ_prob", "wmean_AUC" or "committee_avg"
Details
We implemented a bestiary of plots to help visualizing the process and results. If you are not familiar with mapview, consider using it to better visualize maps.