xglosh: Global-Local Outlier Score from Hierarchies

In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

Global-Local Outlier Score from Hierarchies

Description

Global-Local Outlier Score from Hierarchies

Usage

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

Arguments

data	Data frame of species records with environmental data.
k	The size of the neighborhood (Hahsler et al 2022).
output	Either clean: for data frame with no suspicious outliers or outlier: to return dataframe with only outliers.
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	The different metric distances to compute the distances among the environmental predictors. See dist function and how te different distances are applied. The different measures are allowed including "euclidean", "maximum", "manhattan", "canberra", "binary".
mode	This includes soft when the outliers are removed using mean to compute the z-scores or robust when median absolute deviation.
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.

References

1. Campello, Ricardo JGB, Davoud Moulavi, Arthur Zimek, and Joerg Sander. Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data (TKDD) 10, no. 1 (2015). doi:10.1145/2733381

2. Hahsler M, Piekenbrock M (2022). dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms. R package version 1.1-11, <https://CRAN.R-project.org/package=dbscan>

Examples

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)

gloshout <- xglosh(data = refdata[["Thymallus thymallus"]], exclude = c("x", "y"),
                output='outlier', metric ='manhattan', k = 3,
                 mode = "soft")

specleanr documentation built on Nov. 26, 2025, 1:07 a.m.