Function to detect suspicious outliers based on environmental variables.

Run outlier.tree to detect suspicious outliers in observations.

Usage

suspicious_env_outliers(
  occ,
  occ_crs = 4326,
  variables,
  rm_outliers = FALSE,
  seed = 10L,
  ...,
  visualize = TRUE
)

Arguments

occ: (data.frame, sf, SpatialPointsDataFrame) The occurrence dataset for training. There must be column x and y for coordinates if it is a regular data.frame.
occ_crs: (numeric or crs) The EPSG number or crs object of occurrence CRS. The default value is 4326, which is the geographic coordinate system.
variables: (RasterStack or stars) The stack of environmental variables.
rm_outliers: (logical) The option to remove the suspicious outliers or not. The default is FALSE.
seed: (integer) The random seed used in the modeling. It should be an integer. The default is 10L.
...: Other arguments passed to function outlier.tree in package outliertree.
visualize: (logical) If TRUE, plot the result. The default is TRUE.

Value

(EnvironmentalOutlier) A list that contains

outliers (sf) The sf points of outliers
outlier_details (tibble) A table of outlier details returned from function outlier.tree in package outliertree
pts_occ (sf) The sf points of occurrence. If rm_outliers is TRUE, outliers are deleted from points of occurrence. If FALSE, the full observations are returned.

Details

Please check more details in R documentation of function outlier.tree in package outliertree and their GitHub.

References

Examples

library(dplyr)
library(sf)
library(stars)
library(itsdm)

data("occ_virtual_species")
env_vars <- system.file(
  'extdata/bioclim_tanzania_10min.tif',
  package = 'itsdm') %>% read_stars() %>%
  slice('band', c(1, 5, 12))

occ_outliers <- suspicious_env_outliers(
  occ = occ_virtual_species, variables = env_vars,
  z_outlier = 3.5, outliers_print = 4L, nthreads = 1)

occ_outliers
plot(occ_outliers)