Flags suspicious using the local outlier factor or Density-Based Spatial Clustering of Applications with Noise.

Usage

xlof(
  data,
  output,
  minPts,
  exclude = NULL,
  metric = "manhattan",
  mode = "soft",
  pc = FALSE,
  boot = FALSE,
  var,
  pcvar = NULL
)

Arguments

data: Data frame of species records with environmental data
output: Either clean: for data frame with no suspicious outliers or outlier: to return dataframe with only outliers.
minPts: Minimum neighbors around the records.
exclude: Exclude variables that should not be considered in the fitting the one class model, for example x and y columns or latitude/longitude or any column that the user doesn't want to consider.
metric: Distance-based measure to examine the distance between variables. Default manhattan.
mode: Either soft if mean is used or robust if mad is used. Default soft.
pc: Whether principal component analysis will be computed. Default FALSE
boot: Whether bootstrapping will be computed. Default FALSE
var: The variable of concern, which is vital for univariate outlier detection methods
pcvar: Principal component analysis to e used for outlier detection after PCA. Default PC1

Value

Dataframe with or with no outliers.

Examples


# \donttest{
data("efidata")

danube <- system.file('extdata/danube.shp.zip', package='specleanr')

db <- sf::st_read(danube, quiet=TRUE)

wcd <- terra::rast(system.file('extdata/worldclim.tiff', package='specleanr'))

refdata <- pred_extract(data = efidata, raster= wcd ,
                       lat = 'decimalLatitude',
                       lon= 'decimalLongitude',
                       colsp = "scientificName",
                      bbox = db,
                       minpts = 10)

lofout <- xlof(data = refdata[["Thymallus thymallus"]], exclude = c("x", "y"),
                output='outlier', metric ='manhattan',
                minPts = 10, mode = "soft")
# }